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Sampling properties of estimators of the log‐logistic distribution with application to Canadian precipitation data
Author(s) -
Shoukri M. M.,
Mian I. U. H.,
Tracy D. S.
Publication year - 1988
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3314729
Subject(s) - estimator , quantile , statistics , monte carlo method , mathematics , log logistic distribution , sampling (signal processing) , logistic distribution , moment (physics) , distribution (mathematics) , logistic regression , econometrics , probability distribution , computer science , distribution fitting , physics , mathematical analysis , filter (signal processing) , classical mechanics , computer vision
We consider the probability‐weighted moment and the maximum‐likelihood estimators of two parameters in the log‐logistic distribution. Quantile estimators are obtained using both methods. The distributional properties of these estimators are studied in large samples, via asymptotic theory, and in small and moderate samples, via Monte Carlo simulation. The distribution is shown to be appropriate for a wide variety of meteorological data.