Breadcrumb
An algorithm to compute the power of Monte Carlo tests with guaranteed precision
Fri 01 February 2013, 14:15
Patrick Rubin-Delanchy
Bristol
Organisers: Nick Whiteley, Feng Yu
ABSTRACT
In this talk we present an algorithm that generates a conservative
confidence interval of a specified length and coverage probability for the
power of a Monte Carlo test (such as a bootstrap or permutation test). It
is the first method that achieves this aim for almost any Monte Carlo
test. Previous research has focused on obtaining as accurate a result as
possible for a fixed computational effort, without providing a guaranteed
precision in the above sense. The algorithm we propose does not have a
fixed effort and runs until a confidence interval with a user-specified
length and coverage probability can be constructed. We show that the
expected effort required by the algorithm is finite in most cases of
practical interest, including situations where the distribution of the
p-value is absolutely continuous or discrete with finite support. The
algorithm is implemented in the R-package "simctest", available on CRAN.