This paper presents a decision-theoretic approach
to statistical inference that satisfies the Likelihood Principle (LP) without using prior information. Unlike the Bayesian approach, which also satisfies LP, we do not assume knowledge of the prior distribution of the unknown parameter. With respect to information that can be obtained from an experiment, our solution is more efficient than Waldâ s minimax solution. However, with respect to information assumed to be known before the experiment, our solution demands less input than the Bayesian solution.
This is a short 9-pp version of a longer working paper titled "Decision Making on the Sole Basis of Statistical Likelihood," School of Business Working Paper, Revised November 2004.
Giang, P. H. and P. P. Shenoy (2002), "Statistical Decisions Using Likelihood Information Without Prior Probabilities," in A. Darwiche & N. Friedman (eds.), Uncertainty in Artificial Intelligence (UAI-02), pp. 170-178, Morgan Kaufmann, San Francisco, CA
Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
We want to hear from you! Please
share your stories
about how Open Access to this item benefits YOU.
The University of Kansas prohibits discrimination on the basis of race, color, ethnicity, religion, sex, national origin, age, ancestry, disability, status as a veteran, sexual orientation, marital status, parental status, gender identity, gender expression and genetic information in the University’s programs and activities. The following person has been designated to handle inquiries regarding the non-discrimination policies: Director of the Office of Institutional Opportunity and Access, IOA@ku.edu, 1246 W. Campus Road, Room 153A, Lawrence, KS, 66045, (785)864-6414, 711 TTY.