Abstract
Harris (Biometrika, 1989) suggests a predictive distribution based on bootstrapping using the maximum likelihood estimator of an unknown parameter. Basu and Harris (Biometrika, 1994) introduce robust estimative and bootstrap predictive distributions for discrete models by using the minimum Hellinger distance estimator of the unknown parameter instead of the maximum likelihood estimator. Generalizing the results of Basu and Harris, the present paper considers parametric predictive distributions using the minimum penalized blended weight Hellinger distance estimator for discrete models. Monte Carlo siniulations suggest that the proposed predictive distributions are attractive robust substitutes for the usual predictive distributions based on the maximum likelihood estimator under data contamination, and perform favorably compared to the predictive distributions suggested by Basu and Harris
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Statistics - Simulation and Computation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
