Chen, Feng Maximum local partial likelihood estimators for the counting process intensity function and its derivatives. (English) Zbl 1206.62143 Stat. Sin. 21, No. 1, 107-128 (2011). Summary: We propose estimators for the counting process intensity function and its derivatives by maximizing the local partial likelihood. We prove the consistency and asymptotic normality of the proposed estimators. In addition to the computational ease, a nice feature of the proposed estimators is the automatic boundary bias correction property. We also discuss the choice of the tuning parameters in the definition of the estimators. An effective and easy-to-calculate data-driven bandwidth selector is proposed. A small simulation experiment is carried out to assess the performance of the proposed bandwidth selector and the estimators. Cited in 2 Documents MSC: 62M09 Non-Markovian processes: estimation 62F12 Asymptotic properties of parametric estimators 65C60 Computational problems in statistics (MSC2010) Keywords:asymptotic normality; automatic boundary correction; composite likelihood estimator; consistency; local likelihood; local polynomial methods; martingales; maximum likelihood estimator; multiplicative intensity model; point processes; \(Z\)-estimator Software:invGauss PDFBibTeX XMLCite \textit{F. Chen}, Stat. Sin. 21, No. 1, 107--128 (2011; Zbl 1206.62143) Full Text: DOI Link