Coverage Probability of Random Intervals

TL;DR

This paper develops a general theory for the coverage probability of random intervals based on discrete random variables, showing key properties and applicability to common distributions, enhancing statistical inference accuracy.

Contribution

It introduces a unified theory for coverage probabilities of random intervals involving discrete variables, identifying where minima occur and their continuity properties.

Findings

Minimum coverage probabilities occur at finite discrete sets.

Coverage probabilities are continuous and unimodal between interval endpoints.

Applicable to binomial, Poisson, negative binomial, and hypergeometric distributions.

Abstract

In this paper, we develop a general theory on the coverage probability of random intervals defined in terms of discrete random variables with continuous parameter spaces. The theory shows that the minimum coverage probabilities of random intervals with respect to corresponding parameters are achieved at discrete finite sets and that the coverage probabilities are continuous and unimodal when parameters are varying in between interval endpoints. The theory applies to common important discrete random variables including binomial variable, Poisson variable, negative binomial variable and hypergeometrical random variable. The theory can be used to make relevant statistical inference more rigorous and less conservative.