Improved Small Sample Model Selection Procedures

This paper is concerned with model selection based on penalized maximized log likelihood functions. Its main emphasis is on how these penalties might be chosen in small samples to give good statistical properties. We explore how some of the more successful principles and practices in hypothesis testing can be used to improve the properties of these model selection procedures. This leads to choosing the penalties in order to control probabilities of different models being selected. Various ways this can be achieved using simulation methods are discussed and a computer algorithm is outlined. Some illustrative Monte Carlo simulations are also reported.


Issue Date:
Sep 01 1996
Publication Type:
Working or Discussion Paper
Record Identifier:
http://ageconsearch.umn.edu/record/267920
Language:
English
Total Pages:
22
Series Statement:
Working Paper 18/96




 Record created 2018-02-06, last modified 2018-02-07

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