Distribution free testing of grouped Bernoulli trials

TL;DR

This paper introduces a novel distribution-free testing method for grouped Bernoulli trials using a rotation technique, enabling effective goodness-of-fit tests with strong convergence and power properties.

Contribution

It applies Khmaladze's rotation to Bernoulli trials with covariates, establishing relationships between group sizes and parameters, and demonstrating improved testing performance.

Findings

Rotation relates group sizes to the number of parameters.

Tests show excellent convergence properties.

High power to reject incorrect null hypotheses.

Abstract

Recently Khmaladze has shown how to `rotate' one empirical process to another. This paper is the first to apply this transform when successive data points are generated by a single distributional family, but with covariates varying over the sample. The application is to Bernoulli trials, and new results show how group sizes rotated are related to the number of parameters, and explore the impact of different types of data generating processes. The utility of the rotation is clear: goodness of fit tests after rotation to a distribution free process are easily computed, show excellent convergence properties, and exhibit high power to reject incorrect null hypotheses.