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2019 Goodness-of-fit testing the error distribution in multivariate indirect regression
Justin Chown, Nicolai Bissantz, Holger Dette
Electron. J. Statist. 13(2): 2658-2685 (2019). DOI: 10.1214/19-EJS1591

Abstract

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.

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Justin Chown. Nicolai Bissantz. Holger Dette. "Goodness-of-fit testing the error distribution in multivariate indirect regression." Electron. J. Statist. 13 (2) 2658 - 2685, 2019. https://doi.org/10.1214/19-EJS1591

Information

Received: 1 December 2018; Published: 2019
First available in Project Euclid: 14 August 2019

zbMATH: 07104727
MathSciNet: MR3992501
Digital Object Identifier: 10.1214/19-EJS1591

Subjects:
Primary: 62G10, 62G30
Secondary: 62G05, 62G08

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Vol.13 • No. 2 • 2019
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