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Merging information for semiparametric density estimation

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>11/2004
<mark>Journal</mark>Journal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number4
Volume66
Number of pages18
Pages (from-to)941-958
Publication statusPublished
Early online date13/10/04
Original languageEnglish

Abstract

Summary. The density ratio model specifies that the likelihood ratio of m−1 probability density functions with respect to the mth is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well‐established techniques to choose the smoothing parameter for the density estimators proposed.