Adaptive semiparametric estimation for single index models with jumps

Zhong-Cheng Han, Jin-Guan Lin and Yan-Yong Zhao

Computational Statistics & Data Analysis, 2020, vol. 151, issue C

Abstract: The single index model is one of the most popular semiparametric models in applied quantitative sciences. This paper studies a single index model with unknown jumps (SIMJ) that occur in the link function. An adaptive semiparametric estimation procedure is proposed for estimating the index coefficient and link function. The asymptotic normality of the resulting estimators for both the parametric and nonparametric parts can be established under some mild conditions and without specifying the error distribution. We show that the resulting estimators are robust and efficient for different error distributions. In particular, a modified EM algorithm is developed to implement the adaptive semiparametric estimation in practice. Numerical simulations and real data analysis are conducted to illustrate the finite sample performance of the proposed approach.

Keywords: Single index model; Modal regression; Modified EM algorithm; Robust and efficient estimation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:151:y:2020:i:c:s0167947320301043

DOI: 10.1016/j.csda.2020.107013

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