On the asymptotic distributions of some test statistics for two-way contingency tables

TL;DR

This paper derives the asymptotic distribution and power functions of Chi-square and distance covariance tests for two-way contingency tables, improving power calculations and understanding their behavior under fixed alternatives.

Contribution

It establishes the asymptotic normality of the Chi-square statistic under fixed alternatives and provides explicit variance formulas, enhancing power analysis methods.

Findings

Derived the asymptotic normality of Chi-square under fixed alternatives

Provided explicit formulas for the variance of the Chi-square statistic

Analyzed the power functions of distance covariance tests through simulations

Abstract

Pearson's Chi-square test is a widely used tool for analyzing categorical data, yet its statistical power has remained theoretically underexplored. Due to the difficulties in obtaining its power function in the usual manner, Cochran (1952) suggested the derivation of its Pitman limiting power, which is later implemented by Mitra (1958) and Meng & Chapman (1966). Nonetheless, this approach is suboptimal for practical power calculations under fixed alternatives. In this work, we solve this long-standing problem by establishing the asymptotic normality of the Chi-square statistic under fixed alternatives and deriving an explicit formula for its variance. For finite samples, we suggest a second-order expansion based on the multivariate delta method to improve the approximations. As a further contribution, we obtain the power functions of two distance covariance tests. We apply our findings to study the statistical power of these tests under different simulation settings.