Goodness-of-fit testing the error distribution in multivariate indirect regression
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Date
2018
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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.
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Keywords
hypothesis testing, regularization, multivariate regression, inverse problems, indirect regression estimator