Testing Many Constraints in Possibly Irregular Models Using Incomplete U-Statistics

TL;DR

This paper introduces a new testing methodology for complex hypotheses involving many constraints, using incomplete U-statistics and Gaussian multiplier bootstrap, effective even in high-dimensional and irregular settings.

Contribution

It develops a general testing approach that handles numerous constraints and irregular models by employing incomplete U-statistics and bootstrap methods.

Findings

Bootstrap approximation is valid for mixed degenerate kernels.

The method controls type I error in irregular, high-dimensional settings.

Applicable to polynomial constraints in U-estimable parameters.

Abstract

We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because the number of relevant constraints may be on the same order or even larger than the number of observed samples. Moreover, standard distributional approximations may be invalid due to irregularities in the null hypothesis. We propose a general testing methodology that aims to circumvent these difficulties. The constraints are estimated by incomplete U-statistics, and we derive critical values by Gaussian multiplier bootstrap. We show that the bootstrap approximation of incomplete U-statistics is valid for kernels that we call mixed degenerate when the number of combinations used to compute the incomplete U-statistic is of the same order as the sample size. It follows that our test controls type I error even in irregular settings. Furthermore, the bootstrap approximation covers high-dimensional settings making our testing strategy applicable for problems with many constraints. The methodology is applicable, in particular, when the constraints to be tested are polynomials in U-estimable parameters. As an application, we consider goodness-of-fit tests of latent tree models for multivariate data.