Merging information for semiparametric density estimation. (English) Zbl 1059.62028
Summary: The density ratio model specifies that the likelihood ratio of \(m-1\) probability density functions with respect to the \(m\)-th is of known parametric form without reference to any parametric model. We study a 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 lead 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.
MSC:
| 62G07 | Density estimation |
| 65C60 | Computational problems in statistics (MSC2010) |
Keywords:
bandwidth; biased sampling; discrete choice models; empirical likelihood; kernel estimator; retrospective samplingReferences:
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