Article thumbnail
Location of Repository

Likelihood inference for location, scale, and shape



If (mu, sigma, Sigma) denote the location, scale, and shape parameters of a continuous variate X, we show how the exact likelihood function L(mu, sigma,Sigma\x(1).... x(n)) based on n independent observed values of X can be displayed and used to make frequency-interpretable inferences about any or all of the three parameters. When interest is confined to fewer than three parameters, "simplifying assumptions" may be needed to preserve accuracy in the frequency interpretation. Such simplifying assumptions mathematically resemble Bayesian priors but their logical status is quite different. The approach used leads towards a "Bayes-Frequentist" compromise. (C) 2002 Elsevier Science B.V. All rights reserved

Topics: QA
OAI identifier:
Sorry, our data provider has not provided any external links therefore we are unable to provide a link to the full text.

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.