首页|Inference on parameters of Watson distributions and application to classification of observations
Inference on parameters of Watson distributions and application to classification of observations
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NSTL
Elsevier
In this paper, we derive a class of equivariant estimators of the directional parameter of the Watson distribution with a known concentration parameter. When all parameters are unknown, we derive restricted maximum likelihood estimators (MLEs) of the concentration parameters and Bayes estimators of the parameters under a noninformative prior. An improved likelihood ratio test is proposed to test equality of directional parameters of several Watson distributions with a common concentration parameter. We derive rules to classify axial data into one of the Watson populations on the hypersphere when all parameters are unknown. We propose classification rules based on the MLEs and the Bayes estimators of the parameters. The likelihood ratio-based rule, predictive Bayes rule, and kernel density classifier have been derived for two Watson populations. Moreover, the rules are compared using simulations. (c) 2021 Elsevier B.V. All rights reserved.
Bayes estimatorClassification ruleEquivariant estimatorImproved likelihood ratio testProbability of misclassificationRestricted MLEDIRECTIONAL-DATA
NSTL
Elsevier
