Parametric-Rate Inference for One-Sided Differentiable Parameters

TL;DR

This paper introduces a method for constructing parametric-rate confidence intervals for the maximum of a collection of parameters, handling non-regular cases and scalable to large datasets with high-dimensional predictors.

Contribution

It develops a simple, efficient technique for non-regular inference of maximal parameters, applicable even when the number of predictors grows with sample size.

Findings

Achieves parametric-rate confidence intervals in non-regular settings.

Scales efficiently to large datasets with high-dimensional predictors.

Handles cases where the number of predictors grows with sample size.

Abstract

Suppose one has a collection of parameters indexed by a (possibly infinite dimensional) set. Given data generated from some distribution, the objective is to estimate the maximal parameter in this collection evaluated at this distribution. This estimation problem is typically non-regular when the maximizing parameter is non-unique, and as a result standard asymptotic techniques generally fail in this case. We present a technique for developing parametric-rate confidence intervals for the quantity of interest in these non-regular settings. We show that our estimator is asymptotically efficient when the maximizing parameter is unique so that regular estimation is possible. We apply our technique to a recent example from the literature in which one wishes to report the maximal absolute correlation between a prespecified outcome and one of p predictors. The simplicity of our technique enables an analysis of the previously open case where p grows with sample size. Specifically, we only require that log(p) grows slower than the square root of n, where n is the sample size. We show that, unlike earlier approaches, our method scales to massive data sets: the point estimate and confidence intervals can be constructed in O(np) time.