Statistical tests based on R\'{e}nyi entropy estimation

TL;DR

This paper introduces new goodness-of-fit tests for specific multivariate distributions using Rényi entropy estimators based on nearest neighbor distances, with proven consistency and convergence properties.

Contribution

It proposes novel entropy-based test statistics for multivariate Student and Pearson type II distributions, including an iterative method for shape parameter estimation.

Findings

Proved L^2-consistency of the proposed statistics.

Analyzed convergence rate and asymptotic distribution via Monte Carlo simulations.

Developed a new iterative method for shape parameter estimation.

Abstract

Entropy and its various generalizations are important in many fields, including mathematical statistics, communication theory, physics and computer science, for characterizing the amount of information associated with a probability distribution. In this paper we propose goodness-of-fit statistics for the multivariate Student and multivariate Pearson type II distributions, based on the maximum entropy principle and a class of estimators for R\'{e}nyi entropy based on nearest neighbour distances. We prove the L^2-consistency of these statistics using results on the subadditivity of Euclidean functionals on nearest neighbour graphs, and investigate their rate of convergence and asymptotic distribution using Monte Carlo methods. In addition we present a novel iterative method for estimating the shape parameter of the multivariate Student and multivariate Pearson type II distributions.