Statistics Surveys Articles (Project Euclid)
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The latest articles from Statistics Surveys on Project Euclid, a site for mathematics and statistics resources.en-usCopyright 2010 Cornell University LibraryEuclid-L@cornell.edu (Project Euclid Team)Thu, 05 Aug 2010 15:41 EDTThu, 14 Apr 2011 08:17 EDThttp://projecteuclid.org/collection/euclid/images/logo_linking_100.gifProject Euclid
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Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules
http://projecteuclid.org/euclid.ssu/1266847666
<strong>Michael P. Fay</strong>, <strong>Michael A. Proschan</strong><p><strong>Source: </strong>Statist. Surv., Volume 4, 1--39.</p><p><strong>Abstract:</strong><br/>
In a mathematical approach to hypothesis tests, we start with a clearly defined set of hypotheses and choose the test with the best properties for those hypotheses. In practice, we often start with less precise hypotheses. For example, often a researcher wants to know which of two groups generally has the larger responses, and either a t-test or a Wilcoxon-Mann-Whitney (WMW) test could be acceptable. Although both t-tests and WMW tests are usually associated with quite different hypotheses, the decision rule and p-value from either test could be associated with many different sets of assumptions, which we call perspectives. It is useful to have many of the different perspectives to which a decision rule may be applied collected in one place, since each perspective allows a different interpretation of the associated p-value. Here we collect many such perspectives for the two-sample t-test, the WMW test and other related tests. We discuss validity and consistency under each perspective and discuss recommendations between the tests in light of these many different perspectives. Finally, we briefly discuss a decision rule for testing genetic neutrality where knowledge of the many perspectives is vital to the proper interpretation of the decision rule.
</p>projecteuclid.org/euclid.ssu/1266847666_Thu, 05 Aug 2010 15:41 EDTThu, 05 Aug 2010 15:41 EDTA survey of cross-validation procedures for model selection
http://projecteuclid.org/euclid.ssu/1268143839
<strong>Sylvain Arlot</strong>, <strong>Alain Celisse</strong><p><strong>Source: </strong>Statist. Surv., Volume 4, 40--79.</p><p><strong>Abstract:</strong><br/>
Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its (apparent) universality. Many results exist on model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results. As a conclusion, guidelines are provided for choosing the best cross-validation procedure according to the particular features of the problem in hand.
</p>projecteuclid.org/euclid.ssu/1268143839_Thu, 05 Aug 2010 15:41 EDTThu, 05 Aug 2010 15:41 EDTFinite mixture models and model-based clustering
http://projecteuclid.org/euclid.ssu/1272547280
<strong>Volodymyr Melnykov</strong>, <strong>Ranjan Maitra</strong><p><strong>Source: </strong>Statist. Surv., Volume 4, 80--116.</p><p><strong>Abstract:</strong><br/>
Finite mixture models have a long history in statistics, having been used to model population heterogeneity, generalize distributional assumptions, and lately, for providing a convenient yet formal framework for clustering and classification. This paper provides a detailed review into mixture models and model-based clustering. Recent trends as well as open problems in the area are also discussed.
</p>projecteuclid.org/euclid.ssu/1272547280_Thu, 05 Aug 2010 15:41 EDTThu, 05 Aug 2010 15:41 EDTDiscrete variations of the fractional Brownian motion in the presence of outliers and an additive noise
http://projecteuclid.org/euclid.ssu/1276260873
<strong>Sophie Achard</strong>, <strong>Jean-François Coeurjolly</strong><p><strong>Source: </strong>Statist. Surv., Volume 4, 117--147.</p><p><strong>Abstract:</strong><br/>
This paper gives an overview of the problem of estimating the Hurst parameter of a fractional Brownian motion when the data are observed with outliers and/or with an additive noise by using methods based on discrete variations. We show that the classical estimation procedure based on the log-linearity of the variogram of dilated series is made more robust to outliers and/or an additive noise by considering sample quantiles and trimmed means of the squared series or differences of empirical variances. These different procedures are compared and discussed through a large simulation study and are implemented in the R package dvfBm.
</p>projecteuclid.org/euclid.ssu/1276260873_Thu, 05 Aug 2010 15:41 EDTThu, 05 Aug 2010 15:41 EDTPrimal and dual model representations in kernel-based learninghttp://projecteuclid.org/euclid.ssu/1282746475<strong>Johan A.K. Suykens</strong>, <strong>Carlos Alzate</strong>, <strong>Kristiaan Pelckmans</strong><p><strong>Source: </strong>Statist. Surv., Volume 4, 148--183.</p><p><strong>Abstract:</strong><br/>
This paper discusses the role of primal and (Lagrange) dual model representations in problems of supervised and unsupervised learning. The specification of the estimation problem is conceived at the primal level as a constrained optimization problem. The constraints relate to the model which is expressed in terms of the feature map. From the conditions for optimality one jointly finds the optimal model representation and the model estimate. At the dual level the model is expressed in terms of a positive definite kernel function, which is characteristic for a support vector machine methodology. It is discussed how least squares support vector machines are playing a central role as core models across problems of regression, classification, principal component analysis, spectral clustering, canonical correlation analysis, dimensionality reduction and data visualization.
</p>projecteuclid.org/euclid.ssu/1282746475_Wed, 25 Aug 2010 10:28 EDTWed, 25 Aug 2010 10:28 EDTIdentifying the consequences of dynamic treatment strategies: A decision-theoretic overviewhttp://projecteuclid.org/euclid.ssu/1289579930<strong>A. Philip Dawid</strong>, <strong>Vanessa Didelez</strong><p><strong>Source: </strong>Statist. Surv., Volume 4, 184--231.</p><p><strong>Abstract:</strong><br/>
We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work by Robins and others: in particular, we show how Robins’s ‘ G -computation’ algorithm arises naturally from this decision-theoretic perspective. Careful attention is paid to the mathematical and substantive conditions required to justify the use of this formula. These conditions revolve around a property we term stability , which relates the probabilistic behaviours of observational and interventional regimes. We show how an assumption of ‘sequential randomization’ (or ‘no unmeasured confounders’), or an alternative assumption of ‘sequential irrelevance’, can be used to infer stability. Probabilistic influence diagrams are used to simplify manipulations, and their power and limitations are discussed. We compare our approach with alternative formulations based on causal DAGs or potential response models. We aim to show that formulating the problem of assessing dynamic treatment strategies as a problem of decision analysis brings clarity, simplicity and generality.
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Titterington, eds.) <b>9</b> 281–288. <a href="http://tinyurl.com/33lmuj7">http://tinyurl.com/33lmuj7</a><br/><br/>Henderson, R., Ansel, P. and Alshibani, D. (2010). Regret-regression for optimal dynamic treatment regimes. <i>Biometrics</i> (to appear). doi:10.1111/j.1541-0420.2009.01368.x<br/><br/>Hernán, M. A. and Taubman, S. L. (2008). Does obesity shorten life? The importance of well defined interventions to answer causal questions. <i>International Journal of Obesity</i> <b>32</b> S8–S14.<br/><br/>Holland, P. W. (1986). Statistics and causal inference (with Discussion). <i>Journal of the American Statistical Association</i> <b>81</b> 945–970.<br/><br/>Huang, Y. and Valtorta, M. (2006). Identifiability in causal Bayesian networks: A sound and complete algorithm. In <i>AAAI’06: Proceedings of the 21st National Conference on Artificial Intelligence</i> 1149–1154. AAAI Press.<br/><br/>Kang, J. D. Y. and Schafer, J. L. (2007). 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Addison-Wesley, Reading, Massachusetts.<br/><br/>Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods—Application to control of the healthy worker survivor effect. <i>Mathematical Modelling</i> <b>7</b> 1393–1512.<br/><br/>Robins, J. M. (1987). Addendum to “A new approach to causal inference in mortality studies with sustained exposure periods—Application to control of the healthy worker survivor effect”. <i>Computers & Mathematics with Applications</i> <b>14</b> 923–945.<br/><br/>Robins, J. M. (1989). The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In <i>Health Service Research Methodology: A Focus on AIDS</i> ( L. Sechrest, H. Freeman and A. Mulley, eds.) 113–159. NCSHR, U.S. Public Health Service.<br/><br/>Robins, J. M. (1992). Estimation of the time-dependent accelerated failure time model in the presence of confounding factors. <i>Biometrika</i> <b>79</b> 321–324.<br/><br/>Robins, J. M. (1997). Causal inference from complex longitudinal data. In <i>Latent Variable Modeling and Applications to Causality</i>, ( M. Berkane, ed.). <i>Lecture Notes in Statistics</i> <b>120</b> 69–117. Springer-Verlag, New York.<br/><br/>Robins, J. M. (1998). Structural nested failure time models. In <i>Survival Analysis</i>, ( P. K. Andersen and N. Keiding, eds.). <i>Encyclopedia of Biostatistics</i> <b>6</b> 4372–4389. John Wiley and Sons, Chichester, UK.<br/><br/>Robins, J. M. (2000). Robust estimation in sequentially ignorable missing data and causal inference models. In <i>Proceedings of the American Statistical Association Section on Bayesian Statistical Science 1999</i> 6–10.<br/><br/>Robins, J. M. (2004). Optimal structural nested models for optimal sequential decisions. In <i>Proceedings of the Second Seattle Symposium on Biostatistics</i> ( D. Y. Lin and P. Heagerty, eds.) 189–326. Springer, New York.<br/><br/>Robins, J. M., Greenland, S. and Hu, F. C. (1999). Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. <i>Journal of the American Statistical Association</i> <b>94</b> 687–700.<br/><br/>Robins, J. M., Hernán, M. A. and Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. <i>Epidemiology</i> <b>11</b> 550–560.<br/><br/>Robins, J. M. and Wasserman, L. A. (1997). Estimation of effects of sequential treatments by reparameterizing directed acyclic graphs. In <i>Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence</i> ( D. Geiger and P. Shenoy, eds.) 409-420. Morgan Kaufmann Publishers, San Francisco. <a href="http://tinyurl.com/33ghsas">http://tinyurl.com/33ghsas</a><br/><br/>Rosthøj, S., Fullwood, C., Henderson, R. and Stewart, S. (2006). Estimation of optimal dynamic anticoagulation regimes from observational data: A regret-based approach. <i>Statistics in Medicine</i> <b>25</b> 4197–4215.<br/><br/>Shpitser, I. and Pearl, J. (2006a). Identification of conditional interventional distributions. In <i>Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)</i> ( R. Dechter and T. Richardson, eds.). 437–444. AUAI Press, Corvallis, Oregon. <a href="http://tinyurl.com/2um8w47">http://tinyurl.com/2um8w47</a><br/><br/>Shpitser, I. and Pearl, J. (2006b). Identification of joint interventional distributions in recursive semi-Markovian causal models. In <i>Proceedings of the Twenty-First National Conference on Artificial Intelligence</i> 1219–1226. AAAI Press, Menlo Park, California.<br/><br/>Spirtes, P., Glymour, C. and Scheines, R. (2000). <i>Causation, Prediction and Search</i>, Second ed. Springer-Verlag, New York.<br/><br/>Sterne, J. A. C., May, M., Costagliola, D., de Wolf, F., Phillips, A. N., Harris, R., Funk, M. J., Geskus, R. B., Gill, J., Dabis, F., Miro, J. M., Justice, A. C., Ledergerber, B., Fatkenheuer, G., Hogg, R. S., D’Arminio-Monforte, A., Saag, M., Smith, C., Staszewski, S., Egger, M., Cole, S. R. and When To Start Consortium (2009). Timing of initiation of antiretroviral therapy in AIDS-Free HIV-1-infected patients: A collaborative analysis of 18 HIV cohort studies. <i>Lancet</i> <b>373</b> 1352–1363.<br/><br/>Taubman, S. L., Robins, J. M., Mittleman, M. A. and Hernán, M. A. (2009). Intervening on risk factors for coronary heart disease: An application of the parametric <i>g</i>-formula. <i>International Journal of Epidemiology</i> <b>38</b> 1599–1611.<br/><br/>Tian, J. (2008). Identifying dynamic sequential plans. In <i>Proceedings of the Twenty-Fourth Annual Conference on Uncertainty in Artificial Intelligence</i> (UAI-08) ( D. McAllester and A. Nicholson, eds.). 554–561. AUAI Press, Corvallis, Oregon. <a href="http://tinyurl.com/36ufx2h">http://tinyurl.com/36ufx2h</a><br/><br/>Verma, T. and Pearl, J. (1990). Causal networks: Semantics and expressiveness. In <i>Uncertainty in Artificial Intelligence 4</i> ( R. D. Shachter, T. S. Levitt, L. N. Kanal and J. F. Lemmer, eds.) 69–76. North-Holland, Amsterdam.<br/><br/></p>projecteuclid.org/euclid.ssu/1289579930_Fri, 12 Nov 2010 11:39 ESTFri, 12 Nov 2010 11:39 ESTThe ARMA alphabet soup: A tour of ARMA model variantshttp://projecteuclid.org/euclid.ssu/1291731822<strong>Scott H. Holan</strong>, <strong>Robert Lund</strong>, <strong>Ginger Davis</strong><p><strong>Source: </strong>Statist. Surv., Volume 4, 232--274.</p><p><strong>Abstract:</strong><br/>
Autoregressive moving-average (ARMA) difference equations are ubiquitous models for short memory time series and have parsimoniously described many stationary series. Variants of ARMA models have been proposed to describe more exotic series features such as long memory autocovariances, periodic autocovariances, and count support set structures. This review paper enumerates, compares, and contrasts the common variants of ARMA models in today’s literature. After the basic properties of ARMA models are reviewed, we tour ARMA variants that describe seasonal features, long memory behavior, multivariate series, changing variances (stochastic volatility) and integer counts. A list of ARMA variant acronyms is provided.
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Springer, New York.<br/><br/></p>projecteuclid.org/euclid.ssu/1291731822_Tue, 07 Dec 2010 09:23 ESTTue, 07 Dec 2010 09:23 ESTData confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacyhttp://projecteuclid.org/euclid.ssu/1296828958<strong>Gregory J. Matthews</strong>, <strong>Ofer Harel</strong><p><strong>Source: </strong>Statist. Surv., Volume 5, 1--29.</p><p><strong>Abstract:</strong><br/>
There is an ever increasing demand from researchers for access to useful microdata files. However, there are also growing concerns regarding the privacy of the individuals contained in the microdata. Ideally, microdata could be released in such a way that a balance between usefulness of the data and privacy is struck. This paper presents a review of proposed methods of statistical disclosure control and techniques for assessing the privacy of such methods under different definitions of disclosure.
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Statistical analysis of masked data. Journal of Official Statistics 9, 407–426.<br/><br/>Little, R.J.A., Rubin, D.B., 1987. Statistical Analysis with Missing Data. John Wiley & Sons.<br/><br/>Liu, F., Little, R.J.A., 2002. Selective multiple mputation of keys for statistical disclosure control in microdata. In: Proceedings Joint Statistical Meet. pp. 2133–2138.<br/><br/>Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., Vilhuber, L., April 2008. Privacy: Theory meets practice on the map. In: International Conference on Data Engineering. Cornell University Comuputer Science Department, Cornell, USA, p. 10.<br/><br/>Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M., 2007. L-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1 (1), 3.<br/><br/>Manning, A.M., Haglin, D.J., Keane, J.A., 2008. A recursive search algorithm for statistical disclosure assessment. Data Min. Knowl. Discov. 16 (2), 165–196. <br/><br/>Marsh, C., Skinner, C., Arber, S., Penhale, B., Openshaw, S., Hobcraft, J., Lievesley, D., Walford, N., 1991. The case for samples of anonymized records from the 1991 census. Journal of the Royal Statistical Society 154 (2), 305–340.<br/><br/>Matthews, G.J., Harel, O., Aseltine, R.H., 2010a. Assessing database privacy using the area under the receiver-operator characteristic curve. Health Services and Outcomes Research Methodology 10 (1), 1–15.<br/><br/>Matthews, G.J., Harel, O., Aseltine, R.H., 2010b. Examining the robustness of fully synthetic data techniques for data with binary variables. Journal of Statistical Computation and Simulation 80 (6), 609–624.<br/><br/>Moore, Jr., R., 1996. Controlled data-swapping techniques for masking public use microdata. Census Tech Report.<br/><br/>Mugge, R., 1983. Issues in protecting confidentiality in national health statistics. Proceedings of the Section on Survey Research Methods.<br/><br/>Nissim, K., Raskhodnikova, S., Smith, A., 2007. Smooth sensitivity and sampling in private data analysis. In: STOC ’07: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing. pp. 75–84.<br/><br/>Paass, G., 1988. Disclosure risk and disclosure avoidance for microdata. Journal of Business and Economic Statistics 6 (4), 487–500.<br/><br/>Palley, M., Simonoff, J., 1987. The use of regression methodology for the compromise of confidential information in statistical databases. ACM Trans. Database Systems 12 (4), 593–608.<br/><br/>Raghunathan, T.E., Reiter, J.P., Rubin, D.B., 2003. Multiple imputation for statistical disclosure limitation. Journal of Official Statistics 19 (1), 1–16.<br/><br/>Rajasekaran, S., Harel, O., Zuba, M., Matthews, G.J., Aseltine, Jr., R., 2009. Responsible data releases. In: Proceedings 9th Industrial Conference on Data Mining (ICDM). Springer LNCS, pp. 388–400.<br/><br/>Reiss, S.P., 1984. Practical data-swapping: The first steps. CM Transactions on Database Systems 9, 20–37.<br/><br/>Reiter, J.P., 2002. Satisfying disclosure restriction with synthetic data sets. Journal of Official Statistics 18 (4), 531–543.<br/><br/>Reiter, J.P., 2003. Inference for partially synthetic, public use microdata sets. Survey Methodology 29 (2), 181–188.<br/><br/>Reiter, J.P., 2004a. New approaches to data dissemination: A glimpse into the future (?). Chance 17 (3), 11–15.<br/><br/>Reiter, J.P., 2004b. Simultaneous use of multiple imputation for missing data and disclosure limitation. Survey Methodology 30 (2), 235–242.<br/><br/>Reiter, J.P., 2005a. Estimating risks of identification disclosure in microdata. Journal of the American Statistical Association 100, 1103–1112.<br/><br/>Reiter, J.P., 2005b. Releasing multiply imputed, synthetic public use microdata: An illustration and empirical study. Journal of the Royal Statistical Society, Series A: Statistics in Society 168 (1), 185–205.<br/><br/>Reiter, J.P., 2005c. Using CART to generate partially synthetic public use microdata. Journal of Official Statistics 21 (3), 441–462. <br/><br/>Rubin, D.B., 1987. Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons.<br/><br/>Rubin, D.B., 1993. Comment on “Statistical disclosure limitation”. Journal of Official Statistics 9, 461–468.<br/><br/>Rubner, Y., Tomasi, C., Guibas, L.J., 1998. A metric for distributions with applications to image databases. Computer Vision, IEEE International Conference on 0, 59.<br/><br/>Sarathy, R., Muralidhar, K., 2002a. The security of confidential numerical data in databases. Information Systems Research 13 (4), 389–403.<br/><br/>Sarathy, R., Muralidhar, K., 2002b. The security of confidential numerical data in databases. Info. Sys. Research 13 (4), 389–403.<br/><br/>Schafer, J.L., Graham, J.W., 2002. Missing data: Our view of state of the art. Psychological Methods 7 (2), 147–177.<br/><br/>Singh, A., Yu, F., Dunteman, G., 2003. MASSC: A new data mask for limiting statistical information loss and disclosure. In: Proceedings of the Joint UNECE/EUROSTAT Work Session on Statistical Data Confidentiality. pp. 373–394.<br/><br/>Skinner, C., 2009. Statistical disclosure control for survey data. In: Pfeffermann, D and Rao, C.R. eds. Handbook of Statistics Vol. 29A: Sample Surveys: Design, Methods and Applications. pp. 381–396.<br/><br/>Skinner, C., Marsh, C., Openshaw, S., Wymer, C., 1994. Disclosure control for census microdata. Journal of Official Statistics 10, 31–51.<br/><br/>Skinner, C., Shlomo, N., 2008. Assessing identification risk in survey microdata using log-linear models. Journal of the American Statistical Association 103, 989–1001.<br/><br/>Skinner, C.J., Elliot, M.J., 2002. A measure of disclosure risk for microdata. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 64 (4), 855–867.<br/><br/>Smith, A., 2008. Efficient, dfferentially private point estimators. arXiv:0809.4794v1 [cs.CR].<br/><br/>Spruill, N.L., 1982. Measures of confidentiality. Statistics of Income and Related Administrative Record Research, 131–136.<br/><br/>Spruill, N.L., 1983. The confidentiality and analytic usefulness of masked business microdata. In: Proceedings of the Section on Survey Reserach Microdata. American Statistical Association, pp. 602–607.<br/><br/>Sweeney, L., 1996. Replacing personally-identifying information in medical records, the scrub system. In: American Medical Informatics Association. Hanley and Belfus, Inc., pp. 333–337.<br/><br/>Sweeney, L., 1997. Guaranteeing anonymity when sharing medical data, the datafly system. Journal of the American Medical Informatics Association 4, 51–55.<br/><br/>Sweeney, L., 2002a. Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 10 (5), 571–588. <br/><br/>Sweeney, L., 2002b. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 10 (5), 557–570.<br/><br/>Tendick, P., 1991. Optimal noise addition for preserving confidentiality in multivariate data. Journal of Statistical Planning and Inference 27 (2), 341–353.<br/><br/>United Nations Economic Comission for Europe (UNECE), 2007. Manging statistical cinfidentiality and microdata access: Principles and guidlinesof good practice.<br/><br/>Warner, S.L., 1965. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60 (309), 63–69.<br/><br/>Wasserman, L., Zhou, S., 2010. A statistical framework for differential privacy. Journal of the American Statistical Association 105 (489), 375–389.<br/><br/>Willenborg, L., de Waal, T., 2001. Elements of Statistical Disclosure Control. Springer-Verlag.<br/><br/>Woodward, B., 1995. The computer-based patient record and confidentiality. The New England Journal of Medicine, 1419–1422.<br/><br/></p>projecteuclid.org/euclid.ssu/1296828958_Fri, 04 Feb 2011 09:16 ESTFri, 04 Feb 2011 09:16 ESTCurse of dimensionality and related issues in nonparametric functional regressionhttp://projecteuclid.org/euclid.ssu/1302783447<strong>Gery Geenens</strong><p><strong>Source: </strong>Statist. Surv., Volume 5, 30--43.</p><p><strong>Abstract:</strong><br/>
Recently, some nonparametric regression ideas have been extended to the case of functional regression. Within that framework, the main concern arises from the infinite dimensional nature of the explanatory objects. Specifically, in the classical multivariate regression context, it is well-known that any nonparametric method is affected by the so-called “curse of dimensionality”, caused by the sparsity of data in high-dimensional spaces, resulting in a decrease in fastest achievable rates of convergence of regression function estimators toward their target curve as the dimension of the regressor vector increases. Therefore, it is not surprising to find dramatically bad theoretical properties for the nonparametric functional regression estimators, leading many authors to condemn the methodology. Nevertheless, a closer look at the meaning of the functional data under study and on the conclusions that the statistician would like to draw from it allows to consider the problem from another point-of-view, and to justify the use of slightly modified estimators. In most cases, it can be entirely legitimate to measure the proximity between two elements of the infinite dimensional functional space via a semi-metric, which could prevent those estimators suffering from what we will call the “curse of infinite dimensionality”.
</p><p><strong>References:</strong><br/>[1] Ait-Saïdi, A., Ferraty, F., Kassa, K. and Vieu, P. (2008). Cross-validated estimations in the single-functional index model, Statistics, 42, 475–494.<br/><br/>[2] Aneiros-Perez, G. and Vieu, P. (2008). Nonparametric time series prediction: A semi-functional partial linear modeling, J. Multivariate Anal., 99, 834–857.<br/><br/>[3] Baillo, A. and Grané, A. (2009). Local linear regression for functional predictor and scalar response, J. Multivariate Anal., 100, 102–111.<br/><br/>[4] Burba, F., Ferraty, F. and Vieu, P. (2009). <i>k</i>-Nearest Neighbour method in functional nonparametric regression, J. Nonparam. Stat., 21, 453–469.<br/><br/>[5] Cardot, H., Ferraty, F. and Sarda, P. (1999). Functional linear model, Stat. Probabil. Lett., 45, 11–22.<br/><br/>[6] Crambes, C., Kneip, A. and Sarda, P. (2009). Smoothing splines estimators for functional linear regression, Ann. Statist., 37, 35–72.<br/><br/>[7] Delsol, L. (2009). Advances on asymptotic normality in nonparametric functional time series analysis, Statistics, 43, 13–33.<br/><br/>[8] Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and Its Applications, Chapman and Hall, London.<br/><br/>[9] Fan, J. and Zhang, J.-T. (2000). Two-step estimation of functional linear models with application to longitudinal data, J. Roy. Stat. Soc. B, 62, 303–322.<br/><br/>[10] Ferraty, F. and Vieu, P. (2006). Nonparametric Functional Data Analysis, Springer-Verlag, New York.<br/><br/>[11] Ferraty, F., Laksaci, A. and Vieu, P. (2006). Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models, Statist. Inf. Stoch. Proc., 9, 47–76.<br/><br/>[12] Ferraty, F., Mas, A. and Vieu, P. (2007). Nonparametric regression on functional data: inference and practical aspects, Aust. NZ. J. Stat., 49, 267–286.<br/><br/>[13] Ferraty, F., Van Keilegom, I. and Vieu, P. (2010). On the validity of the bootstrap in nonparametric functional regression, Scand. J. Stat., 37, 286–306.<br/><br/>[14] Ferraty, F., Laksaci, A., Tadj, A. and Vieu, P. (2010). Rate of uniform consistency for nonparametric estimates with functional variables, J. Stat. Plan. Inf., 140, 335–352.<br/><br/>[15] Ferraty, F. and Romain, Y. (2011). Oxford handbook on functional data analysis (Eds), Oxford University Press.<br/><br/>[16] Gasser, T., Hall, P. and Presnell, B. (1998). Nonparametric estimation of the mode of a distribution of random curves, J. Roy. Stat. Soc. B, 60, 681–691.<br/><br/>[17] Geenens, G. (2011). A nonparametric functional method for signature recognition, <i>Manuscript</i>.<br/><br/>[18] Härdle, W., Müller, M., Sperlich, S. and Werwatz, A. (2004). Nonparametric and semiparametric models, Springer-Verlag, Berlin.<br/><br/>[19] James, G.M. (2002). Generalized linear models with functional predictors, J. Roy. Stat. Soc. B, 64, 411–432.<br/><br/>[20] Masry, E. (2005). Nonparametric regression estimation for dependent functional data: asymptotic normality, Stochastic Process. Appl., 115, 155–177.<br/><br/>[21] Nadaraya, E.A. (1964). On estimating regression, Theory Probab. Applic., 9, 141–142.<br/><br/>[22] Quintela-Del-Rio, A. (2008). Hazard function given a functional variable: nonparametric estimation under strong mixing conditions, J. Nonparam. Stat., 20, 413–430.<br/><br/>[23] Rachdi, M. and Vieu, P. (2007). Nonparametric regression for functional data: automatic smoothing parameter selection, J. Stat. Plan. Inf., 137, 2784–2801.<br/><br/>[24] Ramsay, J. and Silverman, B.W. (1997). Functional Data Analysis, Springer-Verlag, New York.<br/><br/>[25] Ramsay, J. and Silverman, B.W. (2002). Applied functional data analysis; methods and case study, Springer-Verlag, New York.<br/><br/>[26] Ramsay, J. and Silverman, B.W. (2005). Functional Data Analysis, 2nd Edition, Springer-Verlag, New York.<br/><br/>[27] Stone, C.J. (1982). Optimal global rates of convergence for nonparametric regression, Ann. Stat., 10, 1040–1053.<br/><br/>[28] Watson, G.S. (1964). Smooth regression analysis, Sankhya A, 26, 359–372.<br/><br/>[29] Yeung, D.T., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T. and Rigoll, G. (2004). SVC2004: First International Signature Verification Competition, Proceedings of the International Conference on Biometric Authentication (ICBA), Hong Kong, July 2004.<br/><br/></p>projecteuclid.org/euclid.ssu/1302783447_Thu, 14 Apr 2011 08:17 EDTThu, 14 Apr 2011 08:17 EDTA review of survival treeshttp://projecteuclid.org/euclid.ssu/1315833185<strong>Imad Bou-Hamad</strong>, <strong>Denis Larocque</strong>, <strong>Hatem Ben-Ameur</strong><p><strong>Source: </strong>Statist. Surv., Volume 5, 44--71.</p><p><strong>Abstract:</strong><br/>
This paper presents a non–technical account of the developments in tree–based methods for the analysis of survival data with censoring. This review describes the initial developments, which mainly extended the existing basic tree methodologies to censored data as well as to more recent work. We also cover more complex models, more specialized methods, and more specific problems such as multivariate data, the use of time–varying covariates, discrete–scale survival data, and ensemble methods applied to survival trees. A data example is used to illustrate some methods that are implemented in R.
</p>projecteuclid.org/euclid.ssu/1315833185_Mon, 12 Sep 2011 09:13 EDTMon, 12 Sep 2011 09:13 EDTPrediction in several conventional contextshttp://projecteuclid.org/euclid.ssu/1336481369<strong>Bertrand Clarke</strong>, <strong>Jennifer Clarke</strong><p><strong>Source: </strong>Statist. Surv., Volume 6, 1--73.</p><p><strong>Abstract:</strong><br/>
We review predictive techniques from several traditional branches of statistics. Starting with prediction based on the normal model and on the empirical distribution function, we proceed to techniques for various forms of regression and classification. Then, we turn to time series, longitudinal data, and survival analysis. Our focus throughout is on the mechanics of prediction more than on the properties of predictors.
</p>projecteuclid.org/euclid.ssu/1336481369_Tue, 08 May 2012 08:50 EDTTue, 08 May 2012 08:50 EDTStatistical inference for disordered sphere packingshttp://projecteuclid.org/euclid.ssu/1342701400<strong>Jeffrey Picka</strong><p><strong>Source: </strong>Statist. Surv., Volume 6, 74--112.</p><p><strong>Abstract:</strong><br/>
This paper gives an overview of statistical inference for disordered sphere packing processes. These processes are used extensively in physics and engineering in order to represent the internal structure of composite materials, packed bed reactors, and powders at rest, and are used as initial arrangements of grains in the study of avalanches and other problems involving powders in motion. Packing processes are spatial processes which are neither stationary nor ergodic. Classical spatial statistical models and procedures cannot be applied to these processes, but alternative models and procedures can be developed based on ideas from statistical physics.
Most of the development of models and statistics for sphere packings has been undertaken by scientists and engineers. This review summarizes their results from an inferential perspective.
</p>projecteuclid.org/euclid.ssu/1342701400_Thu, 19 Jul 2012 08:37 EDTThu, 19 Jul 2012 08:37 EDTThe theory and application of penalized methods or Reproducing Kernel Hilbert Spaces made easyhttp://projecteuclid.org/euclid.ssu/1350394596<strong>Nancy Heckman</strong><p><strong>Source: </strong>Statist. Surv., Volume 6, 113--141.</p><p><strong>Abstract:</strong><br/>
The popular cubic smoothing spline estimate of a regression function arises as the minimizer of the penalized sum of squares $\sum_{j}(Y_{j}-\mu(t_{j}))^{2}+\lambda \int_{a}^{b}[\mu''(t)]^{2}\,dt$, where the data are $t_{j},Y_{j}$, $j=1,\ldots,n$. The minimization is taken over an infinite-dimensional function space, the space of all functions with square integrable second derivatives. But the calculations can be carried out in a finite-dimensional space. The reduction from minimizing over an infinite dimensional space to minimizing over a finite dimensional space occurs for more general objective functions: the data may be related to the function $\mu$ in another way, the sum of squares may be replaced by a more suitable expression, or the penalty, $\int_{a}^{b}[\mu''(t)]^{2}\,dt$, might take a different form. This paper reviews the Reproducing Kernel Hilbert Space structure that provides a finite-dimensional solution for a general minimization problem. Particular attention is paid to the construction and study of the Reproducing Kernel Hilbert Space corresponding to a penalty based on a linear differential operator. In this case, one can often calculate the minimizer explicitly, using Green’s functions.
</p>projecteuclid.org/euclid.ssu/1350394596_Tue, 16 Oct 2012 09:36 EDTTue, 16 Oct 2012 09:36 EDTA survey of Bayesian predictive methods for model assessment, selection and comparisonhttp://projecteuclid.org/euclid.ssu/1356628931<strong>Aki Vehtari</strong>, <strong>Janne Ojanen</strong><p><strong>Source: </strong>Statist. Surv., Volume 6, 142--228.</p><p><strong>Abstract:</strong><br/>
To date, several methods exist in the statistical literature for model assessment, which purport themselves specifically as Bayesian predictive methods. The decision theoretic assumptions on which these methods are based are not always clearly stated in the original articles, however. The aim of this survey is to provide a unified review of Bayesian predictive model assessment and selection methods, and of methods closely related to them. We review the various assumptions that are made in this context and discuss the connections between different approaches, with an emphasis on how each method approximates the expected utility of using a Bayesian model for the purpose of predicting future data.
</p>projecteuclid.org/euclid.ssu/1356628931_Thu, 27 Dec 2012 12:22 ESTThu, 27 Dec 2012 12:22 ESTAnalyzing complex functional brain networks: Fusing statistics and network science to understand the brainhttp://projecteuclid.org/euclid.ssu/1382965566<strong>Sean L. Simpson</strong>, <strong>F. DuBois Bowman</strong>, <strong>Paul J. Laurienti</strong><p><strong>Source: </strong>Statist. Surv., Volume 7, 1--36.</p><p><strong>Abstract:</strong><br/>
Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.
</p>projecteuclid.org/euclid.ssu/1382965566_Mon, 28 Oct 2013 09:06 EDTMon, 28 Oct 2013 09:06 EDTErrata: A survey of Bayesian predictive methods for model assessment, selection and comparisonhttp://projecteuclid.org/euclid.ssu/1393423808<strong>Aki Vehtari</strong>, <strong>Janne Ojanen</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 8, , 1--1.</p><p><strong>Abstract:</strong><br/>
Errata for “A survey of Bayesian predictive methods for model assessment, selection and comparison” by A. Vehtari and J. Ojanen, Statistics Surveys , 6 (2012), 142–228. doi:10.1214/12-SS102.
</p>projecteuclid.org/euclid.ssu/1393423808_20140226091022Wed, 26 Feb 2014 09:10 ESTAdaptive clinical trial designs for phase I cancer studieshttp://projecteuclid.org/euclid.ssu/1401369114<strong>Oleksandr Sverdlov</strong>, <strong>Weng Kee Wong</strong>, <strong>Yevgen Ryeznik</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 8, 2--44.</p><p><strong>Abstract:</strong><br/>
Adaptive clinical trials are becoming increasingly popular research designs for clinical investigation. Adaptive designs are particularly useful in phase I cancer studies where clinical data are scant and the goals are to assess the drug dose-toxicity profile and to determine the maximum tolerated dose while minimizing the number of study patients treated at suboptimal dose levels.
In the current work we give an overview of adaptive design methods for phase I cancer trials. We find that modern statistical literature is replete with novel adaptive designs that have clearly defined objectives and established statistical properties, and are shown to outperform conventional dose finding methods such as the 3+3 design, both in terms of statistical efficiency and in terms of minimizing the number of patients treated at highly toxic or nonefficacious doses. We discuss statistical, logistical, and regulatory aspects of these designs and present some links to non-commercial statistical software for implementing these methods in practice.
</p>projecteuclid.org/euclid.ssu/1401369114_20140529091158Thu, 29 May 2014 09:11 EDTLog-concavity and strong log-concavity: A reviewhttp://projecteuclid.org/euclid.ssu/1418134163<strong>Adrien Saumard</strong>, <strong>Jon A. Wellner</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 8, 45--114.</p><p><strong>Abstract:</strong><br/>
We review and formulate results concerning log-concavity and strong-log-concavity in both discrete and continuous settings. We show how preservation of log-concavity and strong log-concavity on $\mathbb{R}$ under convolution follows from a fundamental monotonicity result of Efron (1965). We provide a new proof of Efron’s theorem using the recent asymmetric Brascamp-Lieb inequality due to Otto and Menz (2013). Along the way we review connections between log-concavity and other areas of mathematics and statistics, including concentration of measure, log-Sobolev inequalities, convex geometry, MCMC algorithms, Laplace approximations, and machine learning.
</p>projecteuclid.org/euclid.ssu/1418134163_20141209090925Tue, 09 Dec 2014 09:09 ESTSemi-parametric estimation for conditional independence multivariate finite mixture modelshttp://projecteuclid.org/euclid.ssu/1423229941<strong>Didier Chauveau</strong>, <strong>David R. Hunter</strong>, <strong>Michael Levine</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 9, 1--31.</p><p><strong>Abstract:</strong><br/>
The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data, is the subject of an increasing number of theoretical and algorithmic developments in the statistical literature. After presenting a survey of this literature, including an in-depth discussion of the all-important identifiability results, this article describes and extends an algorithm for estimation of the parameters in these models. The algorithm works for any number of components in three or more dimensions. It possesses a descent property and can be easily adapted to situations where the data are grouped in blocks of conditionally independent variables. We discuss how to adapt this algorithm to various location-scale models that link component densities, and we even adapt it to a particular class of univariate mixture problems in which the components are assumed symmetric. We give a bandwidth selection procedure for our algorithm. Finally, we demonstrate the effectiveness of our algorithm using a simulation study and two psychometric datasets.
</p>projecteuclid.org/euclid.ssu/1423229941_20150206083903Fri, 06 Feb 2015 08:39 EST$M$-functionals of multivariate scatterhttp://projecteuclid.org/euclid.ssu/1426857094<strong>Lutz Dümbgen</strong>, <strong>Markus Pauly</strong>, <strong>Thomas Schweizer</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 9, 32--105.</p><p><strong>Abstract:</strong><br/>
This survey provides a self-contained account of $M$-estimation of multivariate scatter. In particular, we present new proofs for existence of the underlying $M$-functionals and discuss their weak continuity and differentiability. This is done in a rather general framework with matrix-valued random variables. By doing so we reveal a connection between Tyler’s (1987a) $M$-functional of scatter and the estimation of proportional covariance matrices. Moreover, this general framework allows us to treat a new class of scatter estimators, based on symmetrizations of arbitrary order. Finally these results are applied to $M$-estimation of multivariate location and scatter via multivariate $t$-distributions.
</p>projecteuclid.org/euclid.ssu/1426857094_20150320091136Fri, 20 Mar 2015 09:11 EDTSome models and methods for the analysis of observational datahttp://projecteuclid.org/euclid.ssu/1442364037<strong>José A. Ferreira</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 9, 106--208.</p><p><strong>Abstract:</strong><br/>
This article provides a concise and essentially self-contained exposition of some of the most important models and non-parametric methods for the analysis of observational data, and a substantial number of illustrations of their application. Although for the most part our presentation follows P. Rosenbaum’s book, “Observational Studies”, and naturally draws on related literature, it contains original elements and simplifies and generalizes some basic results. The illustrations, based on simulated data, show the methods at work in some detail, highlighting pitfalls and emphasizing certain subjective aspects of the statistical analyses.
</p>projecteuclid.org/euclid.ssu/1442364037_20150915204040Tue, 15 Sep 2015 20:40 EDTStatistical inference for dynamical systems: A reviewhttp://projecteuclid.org/euclid.ssu/1447165229<strong>Kevin McGoff</strong>, <strong>Sayan Mukherjee</strong>, <strong>Natesh Pillai</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 9, 209--252.</p><p><strong>Abstract:</strong><br/>
The topic of statistical inference for dynamical systems has been studied widely across several fields. In this survey we focus on methods related to parameter estimation for nonlinear dynamical systems. Our objective is to place results across distinct disciplines in a common setting and highlight opportunities for further research.
</p>projecteuclid.org/euclid.ssu/1447165229_20151110092034Tue, 10 Nov 2015 09:20 ESTA unified treatment for non-asymptotic and asymptotic approaches to minimax signal detectionhttp://projecteuclid.org/euclid.ssu/1453212290<strong>Clément Marteau</strong>, <strong>Theofanis Sapatinas</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 9, 253--297.</p><p><strong>Abstract:</strong><br/>
We are concerned with minimax signal detection. In this setting, we discuss non-asymptotic and asymptotic approaches through a unified treatment. In particular, we consider a Gaussian sequence model that contains classical models as special cases, such as, direct, well-posed inverse and ill-posed inverse problems. Working with certain ellipsoids in the space of squared-summable sequences of real numbers, with a ball of positive radius removed, we compare the construction of lower and upper bounds for the minimax separation radius (non-asymptotic approach) and the minimax separation rate (asymptotic approach) that have been proposed in the literature. Some additional contributions, bringing to light links between non-asymptotic and asymptotic approaches to minimax signal, are also presented. An example of a mildly ill-posed inverse problem is used for illustrative purposes. In particular, it is shown that tools used to derive ‘asymptotic’ results can be exploited to draw ‘non-asymptotic’ conclusions, and vice-versa.
In order to enhance our understanding of these two minimax signal detection paradigms, we bring into light hitherto unknown similarities and links between non-asymptotic and asymptotic approaches.
</p>projecteuclid.org/euclid.ssu/1453212290_20160119090454Tue, 19 Jan 2016 09:04 ESTA survey of bootstrap methods in finite population samplinghttp://projecteuclid.org/euclid.ssu/1458047831<strong>Zeinab Mashreghi</strong>, <strong>David Haziza</strong>, <strong>Christian Léger</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 10, 1--52.</p><p><strong>Abstract:</strong><br/>
We review bootstrap methods in the context of survey data where the effect of the sampling design on the variability of estimators has to be taken into account. We present the methods in a unified way by classifying them in three classes: pseudo-population, direct, and survey weights methods. We cover variance estimation and the construction of confidence intervals for stratified simple random sampling as well as some unequal probability sampling designs. We also address the problem of variance estimation in presence of imputation to compensate for item non-response.
</p>projecteuclid.org/euclid.ssu/1458047831_20160315091713Tue, 15 Mar 2016 09:17 EDTFundamentals of cone regressionhttp://projecteuclid.org/euclid.ssu/1463663054<strong>Mariella Dimiccoli</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 10, 53--99.</p><p><strong>Abstract:</strong><br/>
Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as an example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Matlab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution.
</p>projecteuclid.org/euclid.ssu/1463663054_20160519090416Thu, 19 May 2016 09:04 EDTA comparison of spatial predictors when datasets could be very largehttp://projecteuclid.org/euclid.ssu/1468952015<strong>Jonathan R. Bradley</strong>, <strong>Noel Cressie</strong>, <strong>Tao Shi</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 10, 100--131.</p><p><strong>Abstract:</strong><br/>
In this article, we review and compare a number of methods of spatial prediction, where each method is viewed as an algorithm that processes spatial data. To demonstrate the breadth of available choices, we consider both traditional and more-recently-introduced spatial predictors. Specifically, in our exposition we review: traditional stationary kriging, smoothing splines, negative-exponential distance-weighting, fixed rank kriging, modified predictive processes, a stochastic partial differential equation approach, and lattice kriging. This comparison is meant to provide a service to practitioners wishing to decide between spatial predictors. Hence, we provide technical material for the unfamiliar, which includes the definition and motivation for each (deterministic and stochastic) spatial predictor. We use a benchmark dataset of $\mathrm{CO}_{2}$ data from NASA’s AIRS instrument to address computational efficiencies that include CPU time and memory usage. Furthermore, the predictive performance of each spatial predictor is assessed empirically using a hold-out subset of the AIRS data.
</p>projecteuclid.org/euclid.ssu/1468952015_20160719141340Tue, 19 Jul 2016 14:13 EDTMeasuring multivariate association and beyondhttp://projecteuclid.org/euclid.ssu/1479351622<strong>Julie Josse</strong>, <strong>Susan Holmes</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 10, 132--167.</p><p><strong>Abstract:</strong><br/>
Simple correlation coefficients between two variables have been generalized to measure association between two matrices in many ways. Coefficients such as the RV coefficient, the distance covariance (dCov) coefficient and kernel based coefficients are being used by different research communities. Scientists use these coefficients to test whether two random vectors are linked. Once it has been ascertained that there is such association through testing, then a next step, often ignored, is to explore and uncover the association’s underlying patterns.
This article provides a survey of various measures of dependence between random vectors and tests of independence and emphasizes the connections and differences between the various approaches. After providing definitions of the coefficients and associated tests, we present the recent improvements that enhance their statistical properties and ease of interpretation. We summarize multi-table approaches and provide scenarii where the indices can provide useful summaries of heterogeneous multi-block data. We illustrate these different strategies on several examples of real data and suggest directions for future research.
</p>projecteuclid.org/euclid.ssu/1479351622_20161116220034Wed, 16 Nov 2016 22:00 ESTBasic models and questions in statistical network analysishttps://projecteuclid.org/euclid.ssu/1504836152<strong>Miklós Z. Rácz</strong>, <strong>Sébastien Bubeck</strong>. <p><strong>Source: </strong>Statistics Surveys, Volume 11, 1--47.</p><p><strong>Abstract:</strong><br/>
Extracting information from large graphs has become an important statistical problem since network data is now common in various fields. In this minicourse we will investigate the most natural statistical questions for three canonical probabilistic models of networks: (i) community detection in the stochastic block model, (ii) finding the embedding of a random geometric graph, and (iii) finding the original vertex in a preferential attachment tree. Along the way we will cover many interesting topics in probability theory such as Pólya urns, large deviation theory, concentration of measure in high dimension, entropic central limit theorems, and more.
</p>projecteuclid.org/euclid.ssu/1504836152_20170907220234Thu, 07 Sep 2017 22:02 EDT