Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation

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• 作者：Nabil Zougab^a ; ^b ; ^{nabilzougab@yahoo.fr" class="auth_mail} ; Smail Adjabi^b ; ^{adjabi@hotmail.com" class="auth_mail} ; C&eacute ; lestin C. Kokonendji^c ; ^{celestin.kokonendji@univ-fcomte.fr" class="auth_mail}
• 关键词：Bandwidth matrix selection ; Integrated squared error ; Inverse Wishart distribution ; Loss function ; Plug-in ; Smoothed cross-validation
• 刊名：Computational Statistics and Data Analysis
• 出版年：July, 2014
• 年：2014
• 卷：75
• 期：Complete
• 页码：28-38
• 全文大小：904 K

文摘

Bandwidth selection in multivariate kernel density estimation has received considerable attention. In addition to classical methods of bandwidth selection, such as plug-in and cross-validation methods, Bayesian approaches have also been previously investigated. Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation is investigated, when the quadratic and entropy loss functions are used. Under the quadratic loss function, the proposed method is evaluated through a simulation study and two real data sets, which were already discussed in the literature. For these real-data applications, very interesting advantages of the proposed method are pointed out.