Specification testing in semi-parametric transformation models
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Nick Kloodt [1] ; Natalie Neumeyer [1] ; Ingrid Van Keilegom [2]
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[1] University of Hamburg
University of Hamburg
Hamburg, Freie und Hansestadt, Alemania
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[2] KU Leuven
KU Leuven
Arrondissement Leuven, Bélgica
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 30, Nº. 4, 2021, págs. 980-1003
- Idioma: inglés
- DOI: 10.1007/s11749-021-00756-0
- Texto completo no disponible (Saber más ...)
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Resumen
In transformation regression models, the response is transformed before fitting a regression model to covariates and transformed response. We assume such a model where the errors are independent from the covariates and the regression function is modeled nonparametrically. We suggest a test for goodness-of-fit of a parametric transformation class based on a distance between a nonparametric transformation estimator and the parametric class. We present asymptotic theory under the null hypothesis of validity of the semi-parametric model and under local alternatives. A bootstrap algorithm is suggested in order to apply the test. We also consider relevant hypotheses to distinguish between large and small distances of the parametric transformation class to the ‘true’ transformation.
