Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81073
Title: A nonparametric regression model for panel count data analysis
Authors: Zhao, H
Zhang, Y
Zhao, X 
Yu, Z
Keywords: Empirical process
Maximum pseudolikelihood estimator
Regression splines
Wheezing
Issue Date: 2019
Publisher: Academia Sinica, Institute of Statistical Science
Source: Statistica sinica, 2019, v.29, p. 809-826 How to cite?
Journal: Statistica sinica 
Abstract: 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.
URI: http://hdl.handle.net/10397/81073
ISSN: 1017-0405
DOI: 10.5705/ss.202016.0534
Rights: Post with permission of the author and publisher.
Appears in Collections:Journal/Magazine Article

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