This book presents a concise introduction to Bartlett and
Bartlett-type corrections of statistical tests and bias correction
of point estimators. The underlying idea behind both groups of
corrections is to obtain higher accuracy in small samples. While
the main focus is on corrections that can be analytically derived,
the authors also present alternative strategies for improving
estimators and tests based on bootstrap, a data resampling
technique and discuss concrete applications to several important
statistical models.
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