The Annals of Statistics

Asymptotic inference for high-dimensional data

Jim Kuelbs and Anand N. Vidyashankar

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In this paper, we study inference for high-dimensional data characterized by small sample sizes relative to the dimension of the data. In particular, we provide an infinite-dimensional framework to study statistical models that involve situations in which (i) the number of parameters increase with the sample size (that is, allowed to be random) and (ii) there is a possibility of missing data. Under a variety of tail conditions on the components of the data, we provide precise conditions for the joint consistency of the estimators of the mean. In the process, we clarify and improve some of the recent consistency results that appeared in the literature. An important aspect of the work presented is the development of asymptotic normality results for these models. As a consequence, we construct different test statistics for one-sample and two-sample problems concerning the mean vector and obtain their asymptotic distributions as a corollary of the infinite-dimensional results. Finally, we use these theoretical results to develop an asymptotically justifiable methodology for data analyses. Simulation results presented here describe situations where the methodology can be successfully applied. They also evaluate its robustness under a variety of conditions, some of which are substantially different from the technical conditions. Comparisons to other methods used in the literature are provided. Analyses of real-life data is also included.

Article information

Ann. Statist., Volume 38, Number 2 (2010), 836-869.

First available in Project Euclid: 19 February 2010

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Zentralblatt MATH identifier

Primary: 60B10: Convergence of probability measures 60B12: Limit theorems for vector-valued random variables (infinite- dimensional case) 60F05: Central limit and other weak theorems 62A01: Foundations and philosophical topics 62H15: Hypothesis testing 62G20: Asymptotic properties 62F40: Bootstrap, jackknife and other resampling methods 92B15: General biostatistics [See also 62P10]

Covariance matrix estimation c_0 functional genomics high-dimensional data infinite-dimensional central limit theorem joint inference large p small n laws of large numbers l_ρ microarrays shrinkage structured covariance matrices


Kuelbs, Jim; Vidyashankar, Anand N. Asymptotic inference for high-dimensional data. Ann. Statist. 38 (2010), no. 2, 836--869. doi:10.1214/09-AOS718.

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