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On Bayesian selection of the best normal population using theKullback–Leibler divergence measure
Author(s) -
Thabane L.,
Safiul Haq M.
Publication year - 1999
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.00116
Subject(s) - divergence (linguistics) , population , selection (genetic algorithm) , kullback–leibler divergence , measure (data warehouse) , mathematics , multivariate statistics , bayesian probability , statistics , set (abstract data type) , computer science , artificial intelligence , data mining , philosophy , linguistics , demography , sociology , programming language
In this paper, we use the Bayesian approach to study the problem of selecting the best population among k different populations π 1 , ..., π k (k≥2) relative to some standard (or control) population π 0 . Here, π 0 is considered to be the population with the desired characteristics. The best population is defined to be the one which is closest to the ideal population π 0 . The procedure uses the idea of minimizing the posterior expected value of the Kullback–Leibler (KL) divergence measure of π i from π 0 . The populations under consideration are assumed to be multivariate normal. An application to regression problems is also presented. Finally, a numerical example using real data set is provided to illustrate the implementation of the selection procedure.