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The asymptotic properties of the maximum‐relevance weighted likelihood estimators
Author(s) -
Hu F.
Publication year - 1997
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315356
Subject(s) - estimator , relevance (law) , maximum likelihood , econometrics , statistics , mathematics , political science , law
We define the maximum‐relevance weighted likelihood estimator (MREWLE) using the relevance‐weighted likelihood function introduced by Hu and Zidek (1995). Furthermore, we establish the consistency of the MREWLE under a wide range of conditions. Our results generalize those of Wald (1948) to both nonidentically distributed random variables and unequally weighted likelihoods (when dealing with independent data sets of varying relevance to the inferential problem of interest). Asymptotic normality is also proven. Applying these results to generalized smoothing model is discussed.