小样本的均匀分布参数的区间估计和假设检验

本文提出了小样本的均匀分布参数的区间估计和假设检验方法 .数值模拟计算后效果良好     

Panel数据模型的参数bootstrap推断

利用广义p值和参数bootstrap方法,研究了Panel数据模型中未知参数的假设检验问题.对于回归系数,基于最小二乘估计和两步估计方法,考虑了非齐次线性假设检验问题.对于方差分量,研究了单边假设检验问题.进而利用Monte Carlo方法进行模拟研究.模拟结果表明,参数bootstrap检验方法既能较好控制犯第一类错误的概率,又具有较高的功效,且大多数情况下优于广义p值检验方法.     

艾拉姆咖分布参数的Bayes估计及检验

首先给出了艾拉姆咖分布在定数截尾场合下参数的极大似然估计;其次由"平均剩余寿命"的概念得到了参数的拟矩估计;然后取共轭先验分布给出了参数的经验Bayes估计、区间估计及假设检验;最后通过实例给出了不同截尾样本下参数的点估计和区间估计.     

带有误差变量混合分布模型的估计

在许多实际同题中，存在一些不可直接观测的变量，对此统计学家们提出了反卷积和混合分布模型来解决这一变量的分布的估计问题。本文对这一问题采用bootstrap模拟方法得出分布函数的估计，并进一步建立该分布函数的非参bootstrap百分位区间，在数值试验中将我们的处理方式与传统的EM算法得到的分布估计和正态逼近区间作比较，数值结果表明用bootstrap模拟方法得到的准确度更好，数值效果更理想。     

关于区间估计与假设检验的最优性

本文首先提出了区间估计的另一个优良性因素——虚假度 ,进而建立或疏理了区间估计与假设检验的优良性、最优性概念 ,然后给出了区间估计与假设检验最优性的关系     

基于截尾数据指数Pareto分布应力-强度模型的可靠性

在Ⅱ型双截尾删失计划下,讨论了当系统被独立的随机施加指数Pareto (EP)压力时的系统可靠性问题.作者给出了系统可靠性参数的不同点估计和区间估计,其中点估计包括一致最小方差无偏估计(UMVUE)和最大似然估计(MLE);区间估计包括精确置信区间,近似置信区间和bootstrap的区间估计.为了评价不同估计方法效果,作者提供数值模拟结果;最后提供了一个真实数据的分析结果来演示本文提出的方法.     

关于区间估计与假设检验的最优性

本首先提出了区间估计的另一个优良性因素－虚假度，进而建立或疏理了区间估计与假设检验的优良性、最优性概念，然后给出了区间估计与假设检验最优性的关系。     

基于截尾数据指数Pareto分布应力-强度模型的可靠性

在II型双截尾删失计划下，讨论了当系统被独立的随机施加指数Pareto （EP）压力时的系统可靠性问题.作者给出了系统可靠性参数的不同点估计和区间估计，其中点估计包括一致最小方差无偏估计（UMVUE）和最大似然估计（MLE）；区间估计包括精确置信区间，近似置信区间和bootstrap的区间估计.为了评价不同估计方法效果，作者提供数值模拟结果；最后提供了一个真实数据的分析结果来演示本文提出的方法.     

两均匀分布总体参数之比的估计

给出了两均匀分布的最大次序统计量的密度函数,并讨论了两均匀分布参数之比的通常区间估计、最短区间估计及假设检验方法.最后,根据实例求出了这两种区间估计及其区间长度,并得出了相应的结论.     

广义区间估计及其最优性(Ⅳ)

本文在提出了广义区间估计以及其上的最优性概念之后 ,证明了广义区间估计与假设检验的最优性之间的关系 ,推演了一个重要区间估计的最优性     

On New Moment Estimation of Parameters of the Gamma Distribution Using its Characterization

In this paper, the more convenient estimators of both parameters of the gamma distribution are proposed by using its characterization, and shown to be more efficient than the maximum likelihood estimator and the moment estimator for small samples. Furthermore, the distribution of the square of the sample coefficient of variation is obtained by computer simulation for some various values of the parameters and sample size, and thus the simulated confidence interval of its shape parameter is established.     

正态条件下带AR(1)-型方差结构GMANOVA-MANOVA模型极大似然估计的小样本特征

研究了一类带一阶自回归(AR(1))-型方差结构的广义多元方差分析-多元方差分析(GMANO VA-MANOVA)模型参数极大似然估计的小样本特征.对带AR(1)-型方差结构GMANOVA-MANOVA模型,文章在正态条件下给出了参数极大似然估计存在的一个充分必要条件,讨论了极大似然估计唯一的充分条件.在该充分条件下,文章证明了相关系数极大似然估计的精确分布只与相关系数有关,并依此给出了自相关系数简单假设H0:ρ=0v.s.H1:ρ≠0的一个不需要叠代计算估计的检验,同时模拟表明该检验为无偏检验且势函数与似然比检验势函数无太大差异.     

基于灰色模型和Bootstrap理论的大规模定制质量控制方法研究

针对大规模定制生产中样本量小而无法直接应用传统控制图判断过程状态的问题,提出基于灰色模型和Bootstrap理论的质量控制方法.首先采用灰色模型,以现有样本为基础进行数据分析,适当扩展样本量;然后基于Bootstrap统计推断方法获得统计量的经验分布;最后建立样本均值和极差控制图,对生产过程的状态进行分析判断.研究表明,新方法可用于大规模定制生产尤其是新型号生产初期的质量预测和控制.     

ARCH(0,1)系数中位无偏估计分布的渐近展开

This paper is concerned with the distributional properties of a median unbiased estimator of ARCH（0,1） coefficient. The exact distribution of the estimator can be easily derived, however its practical calculations are too heavy to implement, even though the middle range of sample sizes. Since the estimator is shown to have asymptotic normality, asymptotic expansions for the distribution and the percentiles of the estimator are derived as the refinements. Accuracies of expansion formulas are evaluated numerically, and the results of which show that we can effectively use the expansion as a fine approximation of the distribution with rapid calculations. Derived expansion are applied to testing hypothesis of stationarity, and an implementation for a real data set is illustrated.     

ARCH(0,1)系数中位无偏估计分布的渐近展开(英文)

This paper is concerned with the distributional properties of a median unbiased estimator of ARCH(0,1)coefficient.The exact distribution of the estimator can be easily derived,however its practical calculations are too heavy to implement, even though the middle range of sample sizes.Since the estimator is shown to have asymptotic normality,asymptotic expansions for the distribution and the percentiles of the estimator are derived as the refinements.Accuracies of expansion formulas are evaluated numerically,and the results of which show that we can effectively use the expansion as a fine approximation of the distribution with rapid calculations.Derived expansion are applied to testing hypothesis of stationarity,and an implementation for a real data set is illustrated.     

Computational testing algorithmic procedure of assessment for lifetime performance index of Pareto products under progressive type I interval censoring

Process capability indices had been widely used to evaluate the process performance to the continuous improvement of quality and productivity. When the lifetime of products possesses a one-parameter Pareto distribution, the larger-the-better lifetime performance index is considered. The maximum likelihood estimator is used to estimate the lifetime performance index based on the progressive type I interval censored sample. The asymptotic distribution of this estimator is also investigated. We use this estimator to develop the new hypothesis testing algorithmic procedure in the condition of known lower specification limit. Finally, two practical examples are given to illustrate the use of this testing algorithmic procedure to determine whether the process is capable.     

几何分布产品全样本场合步进应力加速寿命试验TFR模型下的统计分析

讨论了几何分布产品在步进应力加速试验TFR模型下寿命分布.给出了其寿命分布函数步进形式,在全样本场合利用极大似然估计方法和矩估计方法求出了未知参数的点估计,最后利用计算机模拟说明本文方法的可行性.     

两参数指数分布产品全样本场合下步进应力加速寿命试验损伤失效率模型下的统计分析

本文给出了两参数指数分布产品全样本场合下步进应力加速寿命试验损伤失效率模型下参数的极大似然估计和拟矩估计,并通过Monte-Carlo模拟说明本文方法的可行性.     

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??How to solve the inference problem of candidate database web surveys is an urgent problem to be solved in the development of web survey. In order to solve this problem, the inference method of non-probability sampling based on superpopulation pseudo design and the combined sample is proposed. A superpopulation model is firstly built up to construct pseudo weights for a survey sample of the web candidate database. The estimator of the population mean is then computed according to the combined sample composed of the survey sample of the web candidate database and a probability sample. The variance estimator of the population mean estimator is lastly derived according to the variance estimation theory of the superpopulation model. The Bootstrap and Jackknife methods are also used to compute the variance estimator. And all these variance estimation methods are compared. The research results show that the population mean estimator based on superpopulation pseudo design and the combined sample is better, and has higher efficiency than the estimator only using the probability sample and the weighted estimator only using the survey sample of the web candidate database. The variance estimator computed by using the VM1, VM2 and VM3 method are relatively better.     

Research on Inference of Candidate Database Web Surveys Based on Superpopulation Pseudo Design and the Combined Sample

How to solve the inference problem of candidate database web surveys is an urgent problem to be solved in the development of web survey. In order to solve this problem, the inference method of non-probability sampling based on superpopulation pseudo design and the combined sample is proposed. A superpopulation model is firstly built up to construct pseudo weights for a survey sample of the web candidate database. The estimator of the population mean is then computed according to the combined sample composed of the survey sample of the web candidate database and a probability sample. The variance estimator of the population mean estimator is lastly derived according to the variance estimation theory of the superpopulation model. The Bootstrap and Jackknife methods are also used to compute the variance estimator. And all these variance estimation methods are compared. The research results show that the population mean estimator based on superpopulation pseudo design and the combined sample is better, and has higher efficiency than the estimator only using the probability sample and the weighted estimator only using the survey sample of the web candidate database. The variance estimator computed by using the VM1, VM2 and VM3 method are relatively better.