Merging information for semiparametric density estimation

• 成果类型: Article
• 署名作者: Fokianos, K
• 署名单位: University of Cyprus
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2004.05480.x
• 发表日期: 2004
• 页码: 941-958
• 关键词: large-sample theory; Empirical Likelihood; distributions; models

摘要

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.