Goodness-of-fit testing the error distribution in multivariate indirect regression
Methodology
2018-12-07 v1
Abstract
We propose a goodness-of-fit test for the distribution of errors from a multivariate indirect regression model. The test statistic is based on the Khmaladze transformation of the empirical process of standardized residuals. This goodness-of-fit test is consistent at the root-n rate of convergence, and the test can maintain power against local alternatives converging to the null at a root-n rate.
Cite
@article{arxiv.1812.02409,
title = {Goodness-of-fit testing the error distribution in multivariate indirect regression},
author = {Justin Chown and Nicolai Bissantz and Holger Dette},
journal= {arXiv preprint arXiv:1812.02409},
year = {2018}
}
Comments
23 pages, 4 figures