Open AccessAnalysis of cyclic recurrent event data with multiple event types
Author(s) -
ChienLin Su,
FengChang Lin
Publication year - 2020
Publication title -
japanese journal of statistics and data science/japanese journal of statistics and data science
Language(s) - English
Resource type - Journals
eISSN - 2520-8764
pISSN - 2520-8756
DOI - 10.1007/s42081-020-00088-7
Subject(s) - predictability , estimator , nonparametric statistics , gaussian process , event (particle physics) , mathematics , computer science , event data , statistics , algorithm , gaussian , physics , quantum mechanics , covariate
Recurrent event data frequently arise in practice, and in some cases, the event process has cyclic or periodic components. We propose a semiparametric rate model with multiple event types that have such features. Generalized estimating equations are used for the estimation of regression coefficients after profiling the baseline rate function with a fully nonparametric estimator. The proposed estimators are shown to be consistent and asymptotically Gaussian. Their finite-sample behavior is assessed through simulation experiments. The predictability of the model with and without the cyclic component is also compared. With the cyclic component, our model improves the predictability of a conventional model without the cyclic feature. Data on recurrent fire alarms in Blenheim, New Zealand, are used for illustration purposes.