Robustification of Gaussian Bayes Classifier by the Minimum β-Divergence Method
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DOI: 10.1007/s00357-019-9306-1
Abstract: The goal of classification is to classify new objects into one of the several known populations. A common problem in most of the existing classifiers is that they are very much sensitive to outliers. To… read more here.
Keywords: divergence method; gaussian bayes; performance; minimum divergence ... See more keywords
Robust Wald-type test statistics based on minimum C-divergence estimators
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DOI: 10.1080/00949655.2020.1783665
Abstract: ABSTRACT Maji et al. [Robust statistical inference based on the C-divergence family. Ann Inst Stat Math. 2019;71:1289–1322] introduced the minimum C-divergence estimators and plugging them in the C-divergence measures give test statistics for testing simple… read more here.
Keywords: divergence estimators; test statistics; test; wald type ... See more keywords
Minimum Divergence Estimators, Maximum Likelihood and the Generalized Bootstrap
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DOI: 10.3390/e23020185
Abstract: This paper states that most commonly used minimum divergence estimators are MLEs for suited generalized bootstrapped sampling schemes. Optimality in the sense of Bahadur for associated tests of fit under such sampling is considered. read more here.
Keywords: estimators maximum; likelihood generalized; maximum likelihood; divergence estimators ... See more keywords
Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation
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DOI: 10.3390/e24050686
Abstract: Federated learning is a framework for multiple devices or institutions, called local clients, to collaboratively train a global model without sharing their data. For federated learning with a central server, an aggregation algorithm integrates model… read more here.
Keywords: aggregation; robust aggregation; federated learning; divergence estimation ... See more keywords