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

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. Man...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2022
Matèries:
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
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
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
Accés en línia:ONIX_20220111_9783036514604_610
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Sumari: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.

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