共查询到20条相似文献,搜索用时 62 毫秒
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在左截断右删失数据下,我们基于乘积限估计给出了分位密度估计, 获得了分位密度估计及其导数的重对数律。 相似文献
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基于左截断右删失数据下的乘积限估计构造了分位数固定宽度序贯置信区间及其估计,研究了序贯置信区间估计的渐近性质。作为副产品,获得了分位数估计近邻点的Bahadur表示定理。这个表示定理是推导分位数固定宽度序贯置信区间估计渐近性质的重要基础。同时,在文中,进行了一些计算机模拟试验,证明了左截断右删失数据下分位数估计的序贯方法是效的和精确的。 相似文献
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在右删失数据下,研究了误差具有异方差结构的非参数回归模型,利用局部多项式方法构造了回归函数的加权局部复合分位数回归估计,并得到了该估计的渐近正态性结果,最后通过模拟,当误差为重尾分布时,该估计比局部多项式估计以及核估计表现得更好. 相似文献
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随机删失数据下几种风险率函数估计的渐近性质 总被引:1,自引:0,他引:1
文中对于删失数据下几种不同的风险率函数估计进行了研究。使用与以往不同的方法,在较弱的条件下,改进并扩充了现有文献的结果,获得了这几种风险率函数估计的渐近正态性,一致强弱相合收敛速度以及重对数律且进行了数值模拟。 相似文献
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何书元 《数学年刊A辑(中文版)》1998,(4)
对左截断右删失模型,在不要求分布函数连续的条件下,给出乘积限估计F_n是真分布的一致强相合估计的完整结果.作为推论,还给出右删失模型下的K-M估计和左截断模型下的乘积限估计的一致强相合性的完整结果. 相似文献
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对于截断与删失下的反映变量,我们提出了一类广义乘积限估计,并获得了它的弱收敛性.在回归分析中,利用这类广义乘积限估计来定义一种最小距离的参数估计,并获得了这种参数估计的相合性和渐近正态性. 相似文献
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主要叙述在数据观测不完全的情况下,采用最小二乘法对线性回归模型回归系数的估计及估计量的渐进性质,并给出数据模拟. 相似文献
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On Consistency of the Nearest Neighbor Estimator of the Density Function and Its Applications 下载免费PDF全文
In this paper, we mainly study the consistency of the nearest neighbor estimator of the density function based on asymptotically almost negatively associated samples. The weak consistency,strong consistency, uniformly strong consistency and the convergence rates are established under some mild conditions. As applications, we further investigate the strong consistency and the rate of strong consistency for hazard rate function estimator. 相似文献
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研究了α-混合样本下最近邻密度估计的渐近性质,证明了估计的渐近正态性并且给出了其渐近方差的显式表达式,由此构造了α-混合样本下概率密度的渐近置信区间. 相似文献
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Liu-quanSun 《应用数学学报(英文版)》2003,19(2):229-238
In this paper we consider a fixed design model in which the observations axe subject to left truncation and right censoring. A generalized product-limit estimator for the conditional distribution at a given covaxiate value is proposed, and an almost sure asymptotic representation of this estimator is established. We also obtain the rate of uniform consistency, weak convergence and a modulus of continuity for this estimator.Applications include trimmed mean and quantile function estimators. 相似文献
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Emmanuel Guerre 《Journal of multivariate analysis》2000,75(2):219
This paper deals with nonparametric regression estimation under arbitrary sampling with an unknown distribution. The effect of the distribution of the design, which is a nuisance parameter, can be eliminated by conditioning. An upper bound for the conditional mean squared error of k−NN estimates leads us to consider an optimal number of neighbors, which is a random function of the sampling. The corresponding estimate can be used for nonasymptotic inference and is also consistent under a minimal recurrence condition. Some deterministic equivalents are found for the random rate of convergence of this optimal estimate, for deterministic and random designs with vanishing or diverging densities. The proposed estimate is rate optimal for standard designs. 相似文献
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本文针对双边删失数据讨论了生存函数的非参数Bayes估计问题,运用Fred-holm积分的一些经典结果,文中证明了该估计弱收敛的一个Gaussian过程。 相似文献
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This paper deals with the conditional quantile estimation based on left-truncated and right-censored data.Assuming that the observations with multivariate covariates form a stationary α-mixing sequence,the authors derive the strong convergence with rate,strong representation as well as asymptotic normality of the conditional quantile estimator.Also,a Berry-Esseen-type bound for the estimator is established.In addition,the finite sample behavior of the estimator is investigated via simulations. 相似文献