Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10526672 | Statistics & Probability Letters | 2005 | 13 Pages |
Abstract
It is well known that the tests based on the residual empirical process for fitting an error distribution in regression models are not asymptotically distribution free. One either uses a Monte-Carlo method or a bootstrap method to implement them. Another option is to base tests on the Khmaladze transformation of these processes because it renders them asymptotically distribution free. This note compares Monte-Carlo, naive bootstrap, and the smooth bootstrap methods of implementing the Kolmogorov-Smirnov test with the Khmaladze transformed test. We find that the transformed test outperforms the naive and smooth bootstrap methods in preserving the level. The note also includes a power comparison of these tests.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Hira L. Koul, Lyudmila Sakhanenko,