Arbeitspapier

A martingale-transform goodness-of-fit test for the form of the conditional variance

In the common nonparametric regression model the problem of testing for a specific parametric form of the variance function is considered. Recently Dette and Hetzler (2008) proposed a test statistic, which is based on an empirical process of pseudo residuals. The process converges weakly to a Gaussian process with a complicated covariance kernel depending on the data generating process. In the present paper we consider a standardized version of this process and propose a martingale transform to obtain asymptotically distribution free tests for the corresponding Kolmogorov-Smirnov and Cramer-von-Mises functionals. The finite sample properties of the proposed tests are investigated by means of a simulation study.

Language
Englisch

Bibliographic citation
Series: Technical Report ; No. 2008,07

Subject
nonparametric regression
goodness-of-it test
martingale transform
conditional variance

Event
Geistige Schöpfung
(who)
Dette, Holger
Hetzler, Benjamin
Event
Veröffentlichung
(who)
Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
(where)
Dortmund
(when)
2008

Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Dette, Holger
  • Hetzler, Benjamin
  • Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen

Time of origin

  • 2008

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