Robust Procedures for Estimating and Testing in the Framework of Divergence Measures

• Format: eBook
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Subjects: Bayes error rate; Bayesian decision making; Bhattacharyya coefficient/distance; bias and variance trade-off; classification; composite likelihood; composite minimum density power divergence estimators; concentration bounds; contingency tables; convergence rates; COVID-19 pandemic; CUSUM monitoring; density power divergence; disparity; divergence measures; epidemiology; estimation of α; Friedman–Rafsky test statistic; Galton-Watson branching processes with immigration; GLM model; Hellinger distance; Hellinger integrals; Henze–Penrose divergence; INARCH model; INGARCH model; integer-valued time series; Kullback-Leibler information distance/divergence; large deviations; MDPDE; minimal spanning trees; minimum density power divergence estimator; minimum pseudodistance estimation; mixed-scale data; model selection; monitoring; n/a; numerical minimization; one-parameter exponential family; pearson residuals; power divergences; rare event probabilities; Reference, Information and Interdisciplinary subjects; relative entropy; Renyi divergences; Research and information: general; residual adjustment function; robust change point test; Robustness; S-estimation; SPC; statistical distances; time series of counts; Tukey’s biweight
• ISBN: 9783036514604; 3036514600; 9783036514598; 3036514597
• DOI: 10.3390/books978-3-0365-1459-8

The scope of the contributions to this book will be to present new and original research papers based on MPHIE, MHD, and MDPDE, as well as test statistics based on these estimators from a theoretical and applied point of view in different statistical problems with special emphasis on robustness. Manuscripts given solutions to different statistical problems as model selection criteria based on divergence measures or in statistics for high-dimensional data with divergence measures as loss function are considered. Reviews making emphasis in the most recent state-of-the art in relation to the solution of statistical problems base on divergence measures are also presented.