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Nonparametric estimating equations based on a penalized information criterion
Author(s) -
Li Bing
Publication year - 2000
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 - 10.2307/3315970
Subject(s) - estimator , mathematics , estimating equations , nonparametric statistics , variance (accounting) , statistics , generalized method of moments , minimum variance unbiased estimator , range (aeronautics) , mathematical optimization , materials science , accounting , business , composite material
It has recently been observed that, given the mean‐variance relation, one can improve on the accuracy of the quasi‐likelihood estimator by the adaptive estimator based on the estimation of the higher moments. The estimation of such moments is usually unstable, however, and consequently only for large samples does the improvement become evident. The author proposes a nonparametric estimating equation that does not depend on the estimation of such moments, but instead on the penalized minimization of asymptotic variance. His method provides a strong improvement over the quasi‐likelihood estimator and the adaptive estimators, for a wide range of sample sizes.