Missing responses at random in functional single index model for time series data

Nengxiang Ling (), Lilei Cheng (), Philippe Vieu () and Hui Ding ()
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Nengxiang Ling: Hefei University of Technology
Lilei Cheng: Hefei University of Technology
Philippe Vieu: Institut de Mathématiques, Université Paul Sabatier
Hui Ding: Nanjing University of Finance and Economics

Statistical Papers, 2022, vol. 63, issue 2, No 13, 665-692

Abstract: Abstract In this paper, we first investigate the estimation of the functional single index regression model with missing responses at random for strong mixing time series data. More precisely, the uniform almost complete convergence rate and asymptotic normality of the estimator are obtained respectively under some general conditions. Then, some simulation studies are carried out to show the finite sample performances of the estimator. Finally, a real data analysis about the sea surface temperature is used to illustrate the effectiveness of our methodology.

Keywords: Functional single index model; Uniform almost complete convergence rate; Asymptotic normality; Strong mixing dependence; Missing responses at random (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00362-021-01251-2

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