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Model Verification and the Likelihood Principle

Fletcher, Samuel C. (2013) Model Verification and the Likelihood Principle. [Preprint]

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Abstract

The likelihood principle (LP) is typically understood as a constraint on any measure of evidence arising from a statistical experiment. It is not sufficiently often noted, however, that the LP assumes that the probability model giving rise to a particular concrete data set must be statistically adequate—it must “fit” the data sufficiently. In practice, though, scientists must make modeling assumptions whose adequacy can nevertheless then be verified using statistical tests. My present concern is to consider whether the LP applies to these techniques of model verification. If one does view model verification as part of the inferential procedures that the LP intends to constrain, then there are certain crucial tests of model verification that no known method satisfying the LP can perform. But if one does not, the degree to which these assumptions have been verified is bracketed from the evidential evaluation under the LP. Although I conclude from this that the LP cannot be a universal constraint on any measure of evidence, proponents of the LP may hold out for a restricted version thereof, either as a kind of “ideal” or as defining one among many different forms of evidence.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Fletcher, Samuel C.scfletch@uci.edu
Keywords: Model verification; Mis-specification testing; Hypothesis testing; Likelihood principle; Sufficiency principle; Evidence
Subjects: General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Depositing User: Samuel C. Fletcher
Date Deposited: 20 Apr 2013 01:27
Last Modified: 20 Apr 2013 01:27
Item ID: 9689
Subjects: General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Date: 19 April 2013
URI: http://philsci-archive.pitt.edu/id/eprint/9689

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