Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5097548 | Journal of Econometrics | 2006 | 35 Pages |
Abstract
The technique of Monte Carlo (MC) tests [Dwass (1957, Annals of Mathematical Statistics 28, 181-187); Barnard (1963, Journal of the Royal Statistical Society, Series B 25, 294)] provides a simple method for building exact tests from statistics whose finite sample distribution is intractable but can be simulated (when no nuisance parameter is involved). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing it to statistics whose null distribution involves nuisance parameters [maximized MC (MMC) tests]. Simplified asymptotically justified versions of the MMC method are also proposed: these provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Jean-Marie Dufour,