VOL. 32 · NO. 1 | February 2004
 
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Frontmatter
Ann. Statist. 32 (1), (February 2004)
No abstract available
Ann. Statist. 32 (1), (February 2004)
No abstract available
The 2002 Wald Memorial Lectures
Leo Breiman
Ann. Statist. 32 (1), 1-11, (February 2004) DOI: 10.1214/aos/1079120126
KEYWORDS: trees, AdaBoost, Bayes risk, 62H30, 68T10, 68T05
Consistency in boosting
Vladimir Koltchinskii, Bin Yu
Ann. Statist. 32 (1), 12, (February 2004) DOI: 10.1214/aos/1079120127
Wenxin Jiang
Ann. Statist. 32 (1), 13-29, (February 2004) DOI: 10.1214/aos/1079120128
KEYWORDS: AdaBoost, Bayes error, boosting, consistency, prediction error, VC dimension, 62G99, 68T99
Gábor Lugosi, Nicolas Vayatis
Ann. Statist. 32 (1), 30-55, (February 2004) DOI: 10.1214/aos/1079120129
KEYWORDS: boosting, ‎classification‎, Bayes-risk consistency, penalized model selection, smoothing parameter, convex cost functions, Empirical processes, 60G99, 62C12, 62G99
Tong Zhang
Ann. Statist. 32 (1), 56-85, (February 2004) DOI: 10.1214/aos/1079120130
KEYWORDS: ‎classification‎, consistency, boosting, large margin methods, kernel methods, 62G05, G2H30, 68T05
Discussions of boosting papers, and rejoinders
Peter L. Bartlett, Peter J. Bickel, Peter Bühlmann, Yoav Freund, Jerome Friedman, Trevor Hastie, Wenxin Jiang, Michael J. Jordan, Vladimir Koltchinskii, Gábor Lugosi, Jon D. McAuliffe, Ya'acov Ritov, Saharan Rosset, Robert E. Schapire, Robert Tibshirani, Nicolas Vayatis, Bin Yu, Tong Zhang, Ji Zhu
Ann. Statist. 32 (1), 85-134, (February 2004) DOI: 10.1214/aos/1105988581
KEYWORDS: boosting, 62H30, 68T05
Classification
Alexander B. Tsybakov
Ann. Statist. 32 (1), 135-166, (February 2004) DOI: 10.1214/aos/1079120131
KEYWORDS: ‎classification‎, Statistical learning, aggregation of classifiers, Optimal rates, Empirical processes, margins, complexity of classes of sets, 62G07, 62G08, 62H30, 68T10
Multivariate analysis
Yijun Zuo, Hengjian Cui, Xuming He
Ann. Statist. 32 (1), 167-188, (February 2004) DOI: 10.1214/aos/1079120132
KEYWORDS: asymptotic normality, depth, Breakdown point, efficiency, projection depth, $L$-estimator, robustness, 62E20, 62F12, 62G35, 62F35
Yijun Zuo, Hengjian Cui, Dennis Young
Ann. Statist. 32 (1), 189-218, (February 2004) DOI: 10.1214/aos/1079120133
KEYWORDS: maximum bias, influence function, contamination sensitivity, gross error sensitivity, projection depth, projection median, ‎Weighted mean, robustness, Breakdown point, 62E20, 62G35, 62G20
Soøren Tolver Jensen, Jesper Madsen
Ann. Statist. 32 (1), 219-232, (February 2004) DOI: 10.1214/aos/1079120134
KEYWORDS: Proportional covariances, maximum likelihood estimation, profile likelihood function, natural exponential families, convexity, Jordan algebra, 62H12, 62H15, 62H10, 60H20, 62F10, 17C50, 52A20
Mu Zhu
Ann. Statist. 32 (1), 233-244, (February 2004) DOI: 10.1214/aos/1079120135
KEYWORDS: Density estimation, nonparametric discriminant analysis, 62H40
Nonparametric estimation under order restrictions
Hammou El Barmi, Hari Mukerjee
Ann. Statist. 32 (1), 245-267, (February 2004) DOI: 10.1214/aos/1079120136
KEYWORDS: Type II bias, estimation, weak convergence, cumulative incidence functions, Hypothesis testing, confidence bands, 62G05, 62G30, 62G10, 62P99
Sequential analysis
Feifang Hu, Li-Xin Zhang
Ann. Statist. 32 (1), 268-301, (February 2004) DOI: 10.1214/aos/1079120137
KEYWORDS: Adaptive randomized design, asymptotic normality, randomized play-the-winner rule, urn model, 60F15, 62G10
George V. Moustakides
Ann. Statist. 32 (1), 302-315, (February 2004) DOI: 10.1214/aos/1079120138
KEYWORDS: CUSUM, change point, disorder problem, sequential detection, Kullback-Leibler divergence, 62L10, 62L15, 62C10
Dong Han, Fugee Tsung
Ann. Statist. 32 (1), 316-339, (February 2004) DOI: 10.1214/aos/1079120139
KEYWORDS: statistical process control, change point detection, average run length, 62L10, 62N10
Models and algorithms for discrete data
Joseph B. Lang
Ann. Statist. 32 (1), 340-383, (February 2004) DOI: 10.1214/aos/1079120140
KEYWORDS: Approximate normality, categorical data, equivalent models, estimability, homogeneous constraint, homogeneous statistic, large-sample inference, restricted maximum likelihood, sampling plan, testability, 62H17, 62E20, 62H12, 62H15
David R. Hunter
Ann. Statist. 32 (1), 384-406, (February 2004) DOI: 10.1214/aos/1079120141
KEYWORDS: Bradley-Terry model, Luce's choice axiom, maximum likelihood estimation, MM algorithm, Newton-Raphson, Plackett-Luce model, 62F07, 65D15
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