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Article
Bayesian Analysis of Hypothesis Testing Problems for General Population: A Kullback–Leibler Alternative
Journal of Statistical Planning and Inference
  • Naveen K. Bansal, Marquette University
  • Gholamhossein Hamedani, Marquette University
  • Ru Sheng, Marquette University
Document Type
Article
Language
eng
Format of Original
8 p.
Publication Date
7-1-2012
Publisher
Elsevier
Original Item ID
doi: 10.1016/j.jspi.2012.02.017
Abstract

We consider a hypothesis problem with directional alternatives. We approach the problem from a Bayesian decision theoretic point of view and consider a situation when one side of the alternatives is more important or more probable than the other. We develop a general Bayesian framework by specifying a mixture prior structure and a loss function related to the Kullback–Leibler divergence. This Bayesian decision method is applied to Normal and Poisson populations. Simulations are performed to compare the performance of the proposed method with that of a method based on a classical z-test and a Bayesian method based on the “0–1” loss.

Comments

Accepted version. Journal of Statistical Planning and Inference, Vol. 142, No. 7 (July, 2012): 1991-1998. DOI. © 2012 Elsevier. Used with permission.

Citation Information
Naveen K. Bansal, Gholamhossein Hamedani and Ru Sheng. "Bayesian Analysis of Hypothesis Testing Problems for General Population: A Kullback–Leibler Alternative" Journal of Statistical Planning and Inference (2012) ISSN: 0378-3758
Available at: http://works.bepress.com/naveen_bansal/5/