Journal of the Korean Statistical Society
- 제21권2호
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- Pages.93-110
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- 1992
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- 1226-3192(pISSN)
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- 2005-2863(eISSN)
On the Model Selection Criteria in Normal Distributions
- Chung, Han-Yeong (Department of Statistics, Hallym University, Chunchon, 200-702) ;
- Lee, Kee-Won (Department of Statistics, Hallym University, Chunchon, 200-702)
- 발행 : 1992.12.01
초록
A model selection approach is used to find out whether the mean and the variance of a unique sample are different from the pre-specified values. Normal distribution is selected as an approximating model. Kullback-Leibler discrepancy comes out as a natural measure of discrepancy between the operating model and the approximating model. Several estimates of selection criterion are computed including AIC, TIC, and a coupleof bootstrap estimator of the selection criterion are considered according to the way of resampling. It is shown that a closed form expression is available for the parametric bootstrap estimated cirterion. A Monte Carlo study is provided to give a formal comparison when the operating family itself is normally distributed.
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