Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/75716

Semiparametric Estimation of a Self-exciting Regression Model with an Application in Recurrent Event Data Analysis

Authors Bai, Fangfang
Chen, Feng
Chen, Kani View this author's profile
Issue Date 2015
Source Statistica Sinica , v. 25, (4), October 2015, p. 1503-1526
Summary We consider a semi-parametric self-exciting point process regression model where the excitation function is assumed to be smooth and decreasing but otherwise unspecified, and the baseline intensity is assumed to be a linear function of the regressors. We propose an estimation method for this model based on monotone splines. The estimator for the regression coefficients is shown to be consistent, asymptotically normal, and semi-parametrically efficient. The consistency of the estimator for the nonparametric excitation function is also established. The numerical performance of the estimators was found to be satisfactory through simulation studies. We illustrate the application of the model to a bladder cancer data set.
Subjects
ISSN 1017-0405
Language English
Format Article
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