On convergence rates for quadratic errors in kernel hazard estimation
AbstractVieu (J. Multivariate Anal. 39 (1991) 324) showed that the quadratic errors for kernel estimates of several curves (including distribution and hazard functions) are asymptotically equivalent under strong mixing conditions. In this paper, the convergence rates of the distances between these quadratic errors are investigated in the particular case of the distribution and hazard functions.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 57 (2002)
Issue (Month): 3 (April)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Cox, Dennis D. & Kim, Tae Yoon, 1995. "Moment bounds for mixing random variables useful in nonparametric function estimation," Stochastic Processes and their Applications, Elsevier, vol. 56(1), pages 151-158, March.
- Kim, Tae Yoon & Cox, Denis D., 1997. "A Study on Bandwidth Selection in Density Estimation under Dependence," Journal of Multivariate Analysis, Elsevier, vol. 62(2), pages 190-203, August.
- Vieu, Philippe, 1991. "Quadratic errors for nonparametric estimates under dependence," Journal of Multivariate Analysis, Elsevier, vol. 39(2), pages 324-347, November.
- Kim, T. Y. & Cox, D. D., 1995. "Asymptotic Behaviors of Some Measures of Accuracy in Nonparametric Curve Estimation with Dependent Observations," Journal of Multivariate Analysis, Elsevier, vol. 53(1), pages 67-93, April.
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