Electronic Journal of Statistics

Estimating the error distribution in semiparametric transformation models

Cédric Heuchenne, Rawane Samb, and Ingrid Van Keilegom

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Abstract

In this paper we consider the semiparametric transformation model $\Lambda_{\theta_{o}}(Y)=m(X)+\varepsilon$, where $\theta_{o}$ is an unknown finite dimensional parameter, the function $m(\cdot)=\mathbb{E}(\Lambda_{\theta_{o}}(Y)|X=\cdot)$ is “smooth”, but otherwise unknown, and the covariate $X$ is independent of the error $\varepsilon$. An estimator of the distribution function of $\varepsilon$ is investigated and its weak convergence is proved. The proposed estimator depends on a profile likelihood estimator of $\theta_{o}$ and a nonparametric kernel estimator of $m$. We also evaluate the practical performance of our estimator in a simulation study for several models and sample sizes. Finally, the method is applied to a data set on the scattering of sunlight in the atmosphere.

Article information

Source
Electron. J. Statist., Volume 9, Number 2 (2015), 2391-2419.

Dates
Received: October 2014
First available in Project Euclid: 29 October 2015

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1446124646

Digital Object Identifier
doi:10.1214/15-EJS1057

Mathematical Reviews number (MathSciNet)
MR3417187

Zentralblatt MATH identifier
1327.62257

Subjects
Primary: 62G08: Nonparametric regression
Secondary: 62E20: Asymptotic distribution theory

Keywords
Empirical distribution function kernel smoothing nonparametric regression profile likelihood estimator semiparametric regression transformation model

Citation

Heuchenne, Cédric; Samb, Rawane; Van Keilegom, Ingrid. Estimating the error distribution in semiparametric transformation models. Electron. J. Statist. 9 (2015), no. 2, 2391--2419. doi:10.1214/15-EJS1057. https://projecteuclid.org/euclid.ejs/1446124646


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