Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods

• Andere auteurs: Mielniczuk, Jan (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: MDPI - Multidisciplinary Digital Publishing Institute 2022
• Onderwerpen: Technology: general issues; History of engineering & technology; Mechanical engineering & materials; 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
• Online toegang: DOAB: download the publication; DOAB: description of the publication

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 serie...