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Activity Number: 616
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #320365
Title: Distribution-Dependent and Distribution-Free Confidence Intervals for the Variance
Author(s): Brent Burch*
Companies: Northern Arizona University
Keywords: Bootstrap ; Coverage probability ; Interval estimation ; Large-sample theory ; Nonparametric
Abstract:

Finding an interval estimation procedure for the variance of a population that achieves a specified confidence level can be problematic. If the distribution of the population is known, then a distribution-dependent interval for the variance can be obtained by considering a power transformation of the sample variance. Simulation results suggest that this method produces intervals for the variance that maintain the nominal probability of coverage for a wide variety of distributions. If the underlying distribution is unknown, then the power itself must be estimated prior to forming the endpoints of the interval. The result is a distribution-free confidence interval estimator of the population variance. Simulation studies indicate that the power transformation method compares favorably to the logarithmic transformation method and the nonparametric bias-corrected and accelerated bootstrap method for moderately sized samples.


Authors who are presenting talks have a * after their name.

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