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

© 2022 by the authors; CC BY-NC-ND license

classification; Bayes error rate; Henze–Penrose divergence; Friedman–Rafsky test statistic; convergence rates; bias and variance trade-off; concentration bounds; minimal spanning trees; composite likelihood; composite minimum density power divergence estimators; model selection; model selection; minimum pseudodistance estimation; Robustness; estimation of α; monitoring; numerical minimization; S-estimation; Tukey’s biweight; integer-valued time series; one-parameter exponential family; minimum density power divergence estimator; density power divergence; robust change point test; Galton-Watson branching processes with immigration; Hellinger integrals; power divergences; Kullback-Leibler information distance/divergence; relative entropy; Renyi divergences; epidemiology; COVID-19 pandemic; Bayesian decision making; INARCH(1) model; GLM model; Bhattacharyya coefficient/distance; time series of counts; INGARCH model; SPC; CUSUM monitoring; MDPDE; contingency tables; disparity; mixed-scale data; pearson residuals; residual adjustment function; robustness; statistical distances; Hellinger distance; large deviations; divergence measures; rare event probabilities; n/a