Robust Procedures for Estimating and Testing in the Framework of Divergence Measures
Material type:
ArticleLanguage: English Publication details: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021Description: 1 electronic resource (333 p.)ISBN: - books978-3-0365-1459-8
- 9783036514604
- 9783036514598
- Research & information: general
- 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
Open Access star Unrestricted online access
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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