TY - BOOK ID - 134934591 TI - Robust Procedures for Estimating and Testing in the Framework of Divergence Measures AU - Pardo, Leandro AU - Martin, Nirian PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - classification KW - Bayes error rate KW - Henze–Penrose divergence KW - Friedman–Rafsky test statistic KW - convergence rates KW - bias and variance trade-off KW - concentration bounds KW - minimal spanning trees KW - composite likelihood KW - composite minimum density power divergence estimators KW - model selection KW - minimum pseudodistance estimation KW - Robustness KW - estimation of α KW - monitoring KW - numerical minimization KW - S-estimation KW - Tukey’s biweight KW - integer-valued time series KW - one-parameter exponential family KW - minimum density power divergence estimator KW - density power divergence KW - robust change point test KW - Galton-Watson branching processes with immigration KW - Hellinger integrals KW - power divergences KW - Kullback-Leibler information distance/divergence KW - relative entropy KW - Renyi divergences KW - epidemiology KW - COVID-19 pandemic KW - Bayesian decision making KW - INARCH(1) model KW - GLM model KW - Bhattacharyya coefficient/distance KW - time series of counts KW - INGARCH model KW - SPC KW - CUSUM monitoring KW - MDPDE KW - contingency tables KW - disparity KW - mixed-scale data KW - pearson residuals KW - residual adjustment function KW - robustness KW - statistical distances KW - Hellinger distance KW - large deviations KW - divergence measures KW - rare event probabilities KW - n/a KW - Henze-Penrose divergence KW - Friedman-Rafsky test statistic KW - Tukey's biweight UR - https://www.unicat.be/uniCat?func=search&query=sysid:134934591 AB - 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. ER -