期刊
APPLIED MATHEMATICAL MODELLING
卷 37, 期 12-13, 页码 7374-7385出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2013.02.044
关键词
Bivariate negative binomial; Consistency; Maximum likelihood; Penalized minimum generalized Hellinger distance; Outliers; Srivastava's triple hypergeometric series
资金
- Ministry of Higher Education, Malaysia [FP014-2012A]
Maximum likelihood (ML) estimation is a popular method for parameter estimation when modeling discrete or count observations but unfortunately it may be sensitive to outliers. Alternative robust methods like minimum Hellinger distance (MHD) have been proposed for estimation. However, in the multivariate case, the MHD method leads to computer intensive estimation especially when the joint probability density function is complicated. In this paper, a Hellinger type distance measure based on the probability generating function is proposed as a tool for quick and robust parameter estimation. The proposed method yields consistent estimators, performs well for simulated and real data, and can be computationally much faster than ML or MHD estimation. (C) 2013 Elsevier Inc. All rights reserved.
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