Specification tests in semiparametric transformation models — A multiplier bootstrap approach

Nick Kloodt and Natalie Neumeyer

Computational Statistics & Data Analysis, 2020, vol. 145, issue C

Abstract: Semiparametric transformation models are considered, where after pre-estimation of a parametric transformation of the response the data are modeled by means of nonparametric regression. Subsequent procedures for testing lack-of-fit of the regression function and for significance of covariates are suggested. In contrast to existing procedures, the tests are asymptotically not influenced by the pre-estimation of the transformation in the sense that they have the same asymptotic distribution as in regression models without transformation. Validity of a multiplier bootstrap procedure is shown which is easier to implement and much less computationally demanding than bootstrap procedures based on the transformation model. In a simulation study the superior performance of the procedure in comparison with its existing competitors is demonstrated.

Keywords: Box–Cox transformation; Lack-of-fit test; Multiplier bootstrap; Nonparametric regression; Significance of covariates; U-statistics; Yeo–Johnson transformation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947319302634
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:145:y:2020:i:c:s0167947319302634

DOI: 10.1016/j.csda.2019.106908

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().