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Model selection for environmental data
Author(s) -
Bai Jun,
Jakeman Anthony J.,
McAleer Michael
Publication year - 1990
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.3170010301
Subject(s) - weibull distribution , monte carlo method , nested set model , model selection , selection (genetic algorithm) , statistics , computer science , log normal distribution , mathematics , null hypothesis , set (abstract data type) , data mining , artificial intelligence , relational database , programming language
A practical approach is proposed for model selection and discrimination among nested and non‐nested probability distributions. Some existing problems with traditional model selection approaches are addressed, including standard testing of a null hypothesis against a more general alternative and the use of some well‐known discrimination criteria for non‐nested distributions. A generalized information criterion (GIC) is used to choose from two or more model structures or probability distributions. For each set of random samples, all model structures that do not perform significantly worse than other candidates are selected. The two‐and three‐parameter gamma, Weibull and lognormal distributions are used to compare the discrimination procedures with traditional approaches. Monte Carlo experiments are employed to examine the performances of the criteria and tests over large sets of finite samples. For each distribution, the Monte Carlo procedure is undertaken for various representative sets of parameter values which are encountered in fitting environmental quality data.