Asymptotic inference for high-dimensional data

TL;DR

This paper develops an infinite-dimensional framework for inference in high-dimensional data with small sample sizes, establishing asymptotic normality and constructing test statistics for mean vector analysis, with applications demonstrated through simulations and real data.

Contribution

It introduces a novel infinite-dimensional approach for high-dimensional inference, improving existing consistency results and providing asymptotic normality for complex models with missing data.

Findings

Established conditions for joint consistency of mean estimators.

Derived asymptotic normality results for high-dimensional models.

Developed and validated new test statistics for mean vector hypotheses.

Abstract

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.