Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
Mielniczuk, Jan
Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods - MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (226 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-4298-0 9783036542973 9783036542980
10.3390/books978-3-0365-4298-0 doi
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
Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods - MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (226 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-4298-0 9783036542973 9783036542980
10.3390/books978-3-0365-4298-0 doi
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