Bias corrected maximum likelihood estimator under the Generalized Linear Model for a binary variable
- Authors
- Park, Mingue; Choi, Boseung
- Issue Date
- 2008
- Publisher
- TAYLOR & FRANCIS INC
- Keywords
- bias; likelihood equation; log likelihood function
- Citation
- COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.37, no.8, pp.1507 - 1514
- Indexed
- SCIE
SCOPUS
- Journal Title
- COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
- Volume
- 37
- Number
- 8
- Start Page
- 1507
- End Page
- 1514
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/125493
- DOI
- 10.1080/03610910802063772
- ISSN
- 0361-0918
- Abstract
- 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.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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