Testing strict monotonicity in nonparametric regression
| dc.contributor.author | Birke, Melanie | |
| dc.contributor.author | Dette, Holger | |
| dc.date.accessioned | 2007-02-21T14:44:43Z | |
| dc.date.available | 2007-02-21T14:44:43Z | |
| dc.date.issued | 2007-02-21T14:44:43Z | |
| dc.description.abstract | A new test for strict monotonicity of the regression function is proposed which is based on a composition of an estimate of the inverse of the regression function with a common regression estimate. This composition is equal to the identity if and only if the “true” regression function is strictly monotone, and a test based on an L2-distance is investigated. The asymptotic normality of the corresponding test statistic is established under the null hypothesis of strict monotonicity. AMS Subject Classification: 62G10 | en |
| dc.identifier.uri | http://hdl.handle.net/2003/23300 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-27 | |
| dc.language.iso | en | de |
| dc.subject | Goodness-of-fit test | en |
| dc.subject | Nonparametric regression | en |
| dc.subject | Strictly monotone regression | en |
| dc.subject.ddc | 004 | |
| dc.title | Testing strict monotonicity in nonparametric regression | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access |