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Biometrika 1988 75(4):741-751; doi:10.1093/biomet/75.4.741
© 1988 by Biometrika Trust
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Rank regression with log gamma residuals

S. P. SMITH and K. HAMMOND

Animal Genetics and Breeding Unit, University of New England Armidale, New South Wales 2351, Australia

A robust analysis for field data is proposed using a stratified rank regression model. That analysis follows Pettitt (1982). Alternatively, a new technique is described where residuals are log gamma random variables. The log gamma distribution is approximately normal when the shape parameter is large and this parameter is taken as an integer which is either preselected or estimated by a simple grid search. Algorithms for estimating location parameters by maximizing the likelihood based on within-stratum rank information are described and illustrated using a small data set.

Key Words: Extreme value • Log gamma • Maximum likelihood • Ranks • Regression • Strata


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