On convergence rates for quadratic errors in kernel hazard estimation
Vieu (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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Volume (Year): 57 (2002)
Issue (Month): 3 (April)
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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.
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- Hall, Peter, 1984. "Central limit theorem for integrated square error of multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 14(1), pages 1-16, February.
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