Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods

doi:10.3390/books978-3-0365-4298-0

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Keywords

high-dimensional time series; nonstationarity; network estimation; change points; kernel estimation; high-dimensional regression; loss function; random predictors; misspecification; consistent selection; subgaussianity; generalized information criterion; robustness; statistical learning theory; information theory; entropy; parameter estimation; learning systems; privacy; prediction methods; misclassification risk; model misspecification; penalized estimation; supervised classification; variable selection consistency; archimedean copula; consistency; estimation; extreme-value copula; tail dependency; multivariate analysis; conditional mutual information; CMI; information measures; nonparametric variable selection criteria; gaussian mixture; conditional infomax feature extraction; CIFE; joint mutual information criterion; JMI; generative tree model; Markov blanket; minimum distance estimation; maximum likelihood estimation; influence functions; adaptive splines; B-splines; right-censored data; semiparametric regression; synthetic data transformation; time series; n/a

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Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods

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May 2022

226 pages

ISBN978-3-0365-4297-3 (Hardback)
ISBN978-3-0365-4298-0 (PDF)

https://doi.org/10.3390/books978-3-0365-4298-0

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This is a Reprint of the Special Issue Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods that was published in

Chemistry & Materials Science

Computer Science & Mathematics

Physical Sciences

Summary

This book addresses contemporary statistical inference issues when no or minimal assumptions on the nature of studied phenomenon are imposed. Information theory methods play an important role in such scenarios. The approaches discussed include various high-dimensional regression problems, time series and dependence analyses.