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Numerical estimators of differential entropy and mutual information can be slow to converge as sample size increases. The offset Kozachenko–Leonenko (KLo) method described here implements an offset version of the Kozachenko–Leonenko estimator that can markedly improve convergence. Its use is illustrated in applications to the comparison of trivariate data from successive scene color images and the comparison of univariate data from stereophonic music tracks. Publicly available code for KLo estimation of both differential entropy and mutual information is provided for R, Python, and MATLAB computing environments at https://github.com/imarinfr/klo.

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This chapter serves as a guide to common advanced statistical methods: multiple regression, two-way and three-way analysis of variance, logistic regression, multiple logistic regression, Spearman’s rho correlation, Wilcoxon rank-sum test, and the Kruskal-Wallis test. Each of these is explanations is accompanied by a software guide to show how to conduct these procedures and interpret the results. There is also a brief description of common multivariate procedures.

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Application of offset estimator of differential entropy and mutual information with multivariate data

15 - Applying Statistics to Research and an Introduction to Advanced Statistical Methods

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Application of offset estimator of differential entropy and mutual information with multivariate data

15 - Applying Statistics to Research and an Introduction to Advanced Statistical Methods

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