Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105329
Title: Relevance weighted likelihood for dependent data
Authors: Hu, F. 
Rosenberger, W.F.
Zidek, J.V.
Keywords: Adaptive designs
Asymptotic normality
Consistency
Generalized estimating equations
Martingales
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
URI: http://scholarbank.nus.edu.sg/handle/10635/105329
ISSN: 00261335
Appears in Collections:Staff Publications

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