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Title: Relevance weighted likelihood for dependent data
Authors: Hu, F. 
Rosenberger, W.F.
Zidek, J.V.
Keywords: Adaptive designs
Asymptotic normality
Generalized estimating equations
Nonparametric regression
Smoothing autoregression model
Urn model
Issue Date: 2000
Citation: Hu, F.,Rosenberger, W.F.,Zidek, J.V. (2000). Relevance weighted likelihood for dependent data. Metrika 51 (3) : 223-243. ScholarBank@NUS Repository.
Abstract: The relevance-weighted likelihood function weights individual contributions to the likelihood according to their relevance for the inferential problem of interest. Consistency and asymptotic normality of the weighted maximum likelihood estimator were previously proved for independent sequences of random variables. We extend these results to apply to dependent sequences, and, in so doing, provide a unified approach to a number of diverse problems in dependent data. In particular, we provide a heretofore unknown approach for dealing with heterogeneity in adaptive designs, and unify the smoothing approach that appears in many foundational papers for independent data. Applications are given in clinical trials, psychophysics experiments, time series models, transition models, and nonparametric regression.
Source Title: Metrika
ISSN: 00261335
Appears in Collections:Staff Publications

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