Bayesian Statistics Seminar
North Carolina State University
presents
Eric Reyes
North Carolina State University
"Bayesian Average Error Approach to Sample Size Calculation for Hypothesis Testing (with instruction on creating an R package)"
ABSTRACT
Under the classical statistical framework, sample size calculations for a hypothesis test of interest maintain pre-specified Type-I and Type-II error rates. These methods often suffer from several practical limitations. We propose a framework for hypothesis testing and sample size determination using Bayesian average errors. We consider rejecting the null hypothesis, in favor of the alternative, when a test statistic exceeds a cutoff. We choose the cutoff to minimize a weighted sum of Bayesian average errors and choose the sample size to bound the total error for the hypothesis test. We illustrate this methodology for a design common in medical studies.
In addition, we briefly discuss how to build an R package in Windows using the BAEssd package (which implements our Bayesian average error sample size determination methodology) as an example.
Presentation slides: Part I and Part II
Thursday, September, 01, 2011
4:00 - 5:00 pm
1216 SAS Hall