Goodness-of-fit testing the error distribution in multivariate indirect regression
dc.contributor.author | Chown, Justin | |
dc.contributor.author | Bissantz, Nicolai | |
dc.contributor.author | Dette, Holger | |
dc.date.accessioned | 2018-12-14T14:03:05Z | |
dc.date.available | 2018-12-14T14:03:05Z | |
dc.date.issued | 2018 | |
dc.description.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. | en |
dc.identifier.uri | http://hdl.handle.net/2003/37835 | |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-19830 | |
dc.language.iso | en | de |
dc.relation.ispartofseries | Discussion Paper / SFB823;34/2018 | en |
dc.subject | hypothesis testing | en |
dc.subject | regularization | en |
dc.subject | multivariate regression | en |
dc.subject | inverse problems | en |
dc.subject | indirect regression estimator | en |
dc.subject.ddc | 310 | |
dc.subject.ddc | 330 | |
dc.subject.ddc | 620 | |
dc.title | Goodness-of-fit testing the error distribution in multivariate indirect regression | en |
dc.type | Text | de |
dc.type.publicationtype | workingPaper | de |
dcterms.accessRights | open access | |
eldorado.secondarypublication | false | de |
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