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随机右删失样本核密度估计的渐近最优带宽选择AI 解读

This paper makes two important contributions to the theory of bandwidth selection for kernel density estimators under right censorship. First, an asymptotic representation of the integrated squared error into easily understood variance and squared bias components is given. Second, it is shown that if the bandwidth is chosen by the data-based method of least-squares cross-validation, then it is asymptotically optimal in a compelling sense. A by-product of the first part is an…

右删失数据分位数函数的核型估计量AI 解读

Based on right-censored data from a lifetime distribution F 0, a kernel-type estimator of the quantile function Qo (p) = inf{t: F 0(t) ≧ p}, 0 ≦ p ≦ 1, is proposed. The estimator is defined by , which is smoother than the usual product-limit quantile function , where denotes the product-limit estimator of F 0 from the censored sample. Under the random censorship model and general conditions on hn, K, and F 0, it is shown that Qn (p) is strongly consistent. In addition, an…

摘要暂缺

任意右删失两样本问题中尺度参数比的估计AI 解读

A two-sample version of the Cramér-von Mises statistic for right censored observations is used to obtain a strongly consistent estimator of the ratio of scale parameters of two distributions. This estimator can be easily obtained in a closed form. Under various survival models, simulation studies are performed to show the advantages of the proposed estimator.

右删失观测下递增失效率分布函数的最大似然估计AI 解读

The maximum likelihood estimator of a distribution function with increasing failure rate is derived based on a set of observations subject to arbitrary right censorship. This estimator is defined everywhere on the positive real line while the Kaplan-Meier estimator may not be. The small sample properties of this estimator are indicated by results of a Monte Carlo study for the Weibull distribution.

右删失观测下递增失效率分布函数的最大似然估计AI 解读

The maximum likelihood estimator of a distribution function with increasing failure rate is derived based on a set of observations subject to arbitrary right censorship. This estimator is defined everywhere on the positive real line while the Kaplan-Meier estimator may not be. The small sample properties of this estimator are indicated by results of a Monte Carlo study for the Weibull distribution.