Bias corrected maximum likelihood estimator under the Generalized Linear Model for a binary variable

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초록

Under the generalized linear models for a binary variable, an approximate bias of the maximum likelihood estimator of the coefficient, that is a special case of linear parameter in Cordeiro and McCullagh (1991), is derived without a calculation of the third-order derivative of the log likelihood function. Using the obtained approximate bias of the maximum likelihood estimator, a bias-corrected maximum likelihood estimator is defined. Through a simulation study, we show that the bias-corrected maximum likelihood estimator and its variance estimator have a better performance than the maximum likelihood estimator and its variance estimator.

키워드

biaslikelihood equationlog likelihood function
제목
Bias corrected maximum likelihood estimator under the Generalized Linear Model for a binary variable
저자
Park, MingueChoi, Boseung
DOI
10.1080/03610910802063772
발행일
2008
유형
Article
저널명
Communications in Statistics Part B: Simulation and Computation
37
8
페이지
1507 ~ 1514