Goodness-of-fit testing the error distribution in multivariate indirect regression

被引：1

作者：

Chown, Justin ^{[1
]}

Bissantz, Nicolai ^{[1
]}

Dette, Holger ^{[1
]}

机构：

[1] Ruhr Univ Bochum, Fak Math, Lehrstuhl Stochast, D-44780 Bochum, Germany

来源：

ELECTRONIC JOURNAL OF STATISTICS | 2019年 / 13卷 / 02期

关键词：

Hypothesis testing; indirect regression; inverse problems; multivariate regression; regularization; DECONVOLUTION; CONVERGENCE; ADAPTATION;

D O I：

10.1214/19-EJS1591

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We propose a goodness-of-fit test for the distribution of errors from a multivariate indirect regression model, which we assume belongs to a location-scale family under the null hypothesis. 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.

引用

页码：2658 / 2685

页数：28

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