Bias Correction for Alternating Iterative Maximum Likelihood Estimators
Received:June 26, 2011  Revised:March 27, 2012
Key Words: maximum likelihood estimation (MLE)   alternating iterative maximum likelihood estimator (AIMLE)   asymptotic normality   bias correction.  
Fund Project:Supported by the National Natural Science Foundation of China (Grant Nos.71171035; 71173029; 10931002; 11071035), the Program for New Century Excellent Talents (Grant No.NCET-10-315) and Excellent Talents Program of Liaoning Educational Committee (Grant No.2008RC15).
Author NameAffiliation
Gang YU School of Mathematics and Quantitative Economics, Dongbei University of Finance and Economics, Liaoning 116025, P. R. China 
Wei GAO Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Jilin 130024, P. R. China 
Ningzhong SHI Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Jilin 130024, P. R. China 
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Abstract:
      In this paper, we give a definition of the alternating iterative maximum likelihood estimator (AIMLE) which is a biased estimator. Furthermore we adjust the AIMLE to result in asymptotically unbiased and consistent estimators by using a bootstrap iterative bias correction method as in Kuk (1995). Two examples and simulation results reported illustrate the performance of the bias correction for AIMLE.
Citation:
DOI:10.3770/j.issn:2095-2651.2013.01.001
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