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dc.contributor.authorAllison, J.S.
dc.contributor.authorMeintanis, S.G.
dc.contributor.authorHušková, M.
dc.date.accessioned2017-06-13T09:34:09Z
dc.date.available2017-06-13T09:34:09Z
dc.date.issued2018
dc.identifier.citationAllison, J.S. et al. 2018. Testing the adequacy of semiparametric transformation models. Test, 27(1):70-94. [https://doi.org/10.1007/s11749-017-0544-4]en_US
dc.identifier.issn1133-0686
dc.identifier.issn1863-8260 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/24984
dc.identifier.urihttps://doi.org/10.1007/s11749-017-0544-4
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs11749-017-0544-4
dc.description.abstractWe consider a semiparametric model whereby the response variable following a transformation can be expressed by means of a regression model. In this model, the form of the transformation is specified analytically (up to an unknown transformation parameter), while the regression function is completely unknown. We develop testing procedures for the null hypothesis that this semiparametric model adequately describes the data at hand. In doing so, the test statistic is formulated on the basis of Fourier-type conditional expectations, an idea first put forward by Bierens (J Econom 20:105–134, 1982). The asymptotic distribution of the test statistic is obtained under the null as well as under alternative hypotheses. Since the limit null distribution is nonstandard, a bootstrap version is utilized in order to actually carry out the test procedure. Monte Carlo results are included that illustrate the finite-sample properties of the new methoden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectTransformation modelen_US
dc.subjectGoodness-of-fit testen_US
dc.subjectNonparametric regressionen_US
dc.subjectBootstrap testen_US
dc.titleTesting the adequacy of semiparametric transformation modelsen_US
dc.typeArticleen_US
dc.contributor.researchID11985682 - Allison, James Samuel
dc.contributor.researchID21262977 - Meintanis, Simos George


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