Merging information for semiparametric density estimation

Fokianos, K. (2004) Merging information for semiparametric density estimation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66 (4). pp. 941-958. ISSN 1369-7412

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
127886
Deposited By:
Deposited On:
03 Oct 2018 12:58
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Sep 2021 04:00