Kernel Density and Hazard Rate Estimation for Censored Dependent Data
AbstractIn some long term studies, a series of dependent and possibly censored failure times may be observed. Suppose that the failure times have a common marginal distribution function having a density, and the nonparametric estimation of density and hazard rate under random censorship is of our interest. In this paper, we establish the asymptotic normality and the uniform consistency (with rates) of the kernel estimators for density and hazard function under a censored dependent model. A numerical study elucidates the behavior of the estimators for moderately large sample sizes.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 67 (1998)
Issue (Month): 1 (October)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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CORE Discussion Papers
2006118, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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- R. Maya & E. Abdul-Sathar & G. Rajesh & K. Muraleedharan Nair, 2014. "Estimation of the Renyi’s residual entropy of order $$\alpha $$ with dependent data," Statistical Papers, Springer, vol. 55(3), pages 585-602, August.
- Sun, Liuquan & Zhou, Xian, 2001. "Survival function and density estimation for truncated dependent data," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 47-57, March.
- Fakoor, V., 2010. "Strong uniform consistency of kernel density estimators under a censored dependent model," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 318-323, March.
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