@article{68accd10e3654861858288c26d46f717,
title = "Estimation of the error distribution function for partial linear single-index models",
abstract = "We consider the estimation of the error distribution function of partial linear single-index models. The estimation methods for the error distribution function based on the classical empirical distribution function as well as empirical likelihood method are discussed, the latter method allows for incorporation of additional information on the error distribution function into estimation. We show weak convergence of the corresponding empirical processes to Gaussian processes and compare both approaches with the asymptotic theory and by means of simulation studies.",
keywords = "Efficient estimator, Empirical distribution function, Empirical likelihood, Kernel smoothing, Single-index",
author = "Jun Zhang and Cuizhen Niu and Tao Lu and Zhenghong Wei",
note = "Funding Information: Cuizhen Niu{\textquoteright}s research was supported by the National Natural Science Foundation of China (Grant No. 11701034) and the Fundamental Research Funds for the Central Universities, China Postdoctoral Science Foundation (Grant No. 2016M600951). Jun Zhang{\textquoteright}s research was supported by the National Natural Science Foundation of China (Grant No. 11401391). The authors thank the editor, the associate editor, and two referees for their constructive suggestions that helped us to improve the early manuscript. Publisher Copyright: {\textcopyright} 2018, {\textcopyright} 2018 Taylor & Francis Group, LLC.",
year = "2020",
month = jan,
day = "2",
doi = "10.1080/03610918.2018.1468461",
language = "English",
volume = "49",
pages = "29--44",
journal = "Communications in Statistics - Simulation and Computation",
issn = "0361-0918",
publisher = "Taylor & Francis Online",
number = "1",
}