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Research Report SRR96-003
A note on parametric bootstrap model selection
Sin-Ho Jung, Kee-Won Lee, Ja-Yong Koo
Abstract:
We develop parametric bootstrap model selection criteria
in an example to fit a random sample to
either a general normal distribution or a normal distribution
with prespecified mean. We apply the bootstrap methods
in two ways; one considers direct substitution
of the estimated parameter for the unknown parameter,
and the other focuses on bias correction.
These bootstrap model selection criteria are
compared with AIC. We illustrate that all the selection
rules reduce to the one sample t-test, where the cutoff
points converge as the sample size increases.
Primary AMS Classification: 62G09
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