A NONPARAMETRIC REGRESSION MODEL FOR PANEL COUNT DATA ANALYSIS | Zendy

Huadong Zhao | Zendy; Ying Zhang | Zendy; Xingqiu Zhao | Zendy; Zhangsheng Yu | Zendy

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A NONPARAMETRIC REGRESSION MODEL FOR PANEL COUNT DATA ANALYSIS

Author(s) -

Huadong Zhao,

Ying Zhang,

Xingqiu Zhao,

Zhangsheng Yu

Publication year - 2017

Publication title -

statistica sinica

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.24

H-Index - 77

eISSN - 1996-8507

pISSN - 1017-0405

DOI - 10.5705/ss.202016.0534

Subject(s) - count data , panel data , statistics , regression analysis , nonparametric statistics , nonparametric regression , econometrics , semiparametric regression , regression , computer science , mathematics , poisson distribution

Panel count data are commonly encountered in analysis of recurrent events where the exact event times are unobserved. To accommodate the potential non-linear covariate effect, we consider a non-parametric regression model for panel count data. The regression B-splines method is used to estimate the regression function and the baseline mean function. The B-splines-based estimation is shown to be consistent and the rate of convergence is obtained. Moreover, the asymptotic normality for a class of smooth functionals of regression splines estimators is established. Numerical studies were carried out to evaluate the finite sample properties. Finally, we applied the proposed method to analyze the non-linear effect of one of interleukin functions with the risk of childhood wheezing.

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