Bayesian analysis of outliers via akaike's predictive likelihood of a model
作者:
Genshiro Kitagawa,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1984)
卷期:
Volume 13,
issue 1
页码: 107-126
ISSN:0361-0918
年代: 1984
DOI:10.1080/03610918408812361
出版商: Marcel Dekker, Inc.
关键词: outlier detection;expected log likelihood;AIC;mean shift;quasi-Bayesian procedure
数据来源: Taylor
摘要:
A set of independent observations is assumed to come from one or more normal populations having the same unknown variance and different unknown means. Ignorance priors are associated with these parameters. The number of populations is also unknown as is the number of observations from each, but priors are chosen for these quantities which make it very likely that one population is dominant. Observations from the rest are considered outliers. Using these priors in conjunction with Akaike's predictive likelihood, which is derived for the class of models considered, one can obtain a quasi-Bayesian posterior probability for each possible model. A “robust” estimate of the mean value of the dominant population and “corrected” values for the outliers can be calculated from the posterior probabilities, once the outliers have been designated. Darwin's data and Herndon's data are analyzed to illustrate the procedure.
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