二分类集群数据下灵敏度和特异度的置信区间构建

在医学诊断等应用领域中广泛存在二分类集群数据,其特征是来自同一个群的反应结果存在相关。对于该数据下灵敏度和特异度的置信区间构造,目前已有方法在小样本及灵敏度或特异度偏大时区间覆盖率较差,通过利用二项分布得分区间的构造思想,基于灵敏度和特异度的最优加权估计量构造一种新的置信区间;通过蒙特卡洛模拟表明,与已有方法相比新区间的覆盖率明显最优、且区间长度较小;新区间在二分类集群数据的应用中值得推广。     

简单均匀分布参数同等最短置信区间的求法

对形如U(0,θ)的均匀分布,文章在给定置信水平1-a下,用计算函数极值的方法得到了参数q的平均长度最短的同等置信区间,然后通过最大密度区间法得到了该参数的相同的最短置信区间,后者的求解过程也充分印证了该方法也是确定参数最短置信区间以及构造等尾置信区间的依据.     

相关差别指标的区间估计方法比较

在公共疾病控制领域，重大稀有疾病的发病率非常低，符合逆抽样特征，量化分析重大稀有疾病的发病率并对其特点进行分析。为了研究在带有群内相关条件下的整群抽样问题，通过二项分布抽样对比流行病学中相关差别指标的六种渐近置信区间的构造方法研究，综合考虑实际覆盖率与区间长度对各种方法的优劣及适用情况做出对比分析。研究表明，Wald型置信区间与对数变换的置信区间对发病率的估计表现因参数而定，而Bootstrap类方法不稳定。本研究找出了不同区间估计方法的适用场合，应合理看待置信区间这种评估方法在流行病学中的实际应用。     

相关差别指标的区间估计方法比较

在公共疾病控制领域,重大稀有疾病的发病率非常低,符合逆抽样特征,量化分析重大稀有疾病的发病率并对其特点进行分析,为了研究在带有群内相关条件下的整群抽样问题,通过β-二项分布抽样对比流行病学中相关差别指标的六种渐近置信区间的构造方法,综合考虑实际覆盖率与区间长度对各种方法的优劣及适用情况并对比分析。研究表明,Wald型置信区间与对数变换的置信区间对发病率的估计表现因参数而定,而Bootstrap类方法不稳定。本研究找出了不同区间估计方法的适用场合,认为应合理看待置信区间这种评估方法在流行病学中的实际应用。     

参数均未知时两均匀分布区间长度比的估计

对于两个均匀分布总体。当参数都未知且取自这两个总体的样本容量不同时，文章给出了区间长度之比的无偏估计，采用等尾法给出了区间估计，并进一步讨论了区间长度之比的最短置信区间。     

二项分布下基于鞍点逼近的总体成数置信区间的构造

面对总体成数置信区间的估计问题,可以采用二项分布下基于鞍点逼近的方法来构造总体成数的置信区间,这种方法为总体成数的区间估计提供了一种新的途径,将其和传统的区间估计方法比较,即正态近似法和枢轴量法进行比较。蒙特卡洛模拟和实例分析的结果为:在几种不同的置信区间构造方法中,小样本情况下,鞍点逼近方法构造的总体成数的置信区间长度相对较短,覆盖率最接近名义水平;大样本下,鞍点逼近方法整体表现最优。因此,可以得到鞍点逼近法对总体成数置信区间的估计较为精确,尤其是小样本情况下更为适用的结论。     

负二项分布参数的区间估计

文章研究了负二项分布的成功概率的区间估计,给出了成功概率的精确置信区间、不依赖于大样本的近似置信区间以及依赖于大样本的近似置信区间.     

二项分布参数的Bayes区间估计问题研究

在区间估计中,当给定置信概率时,区间长度越短精度就越高。在R＋上取值的参数HPD区间估计已被很多人研究。文章给出了二项分布中成功概率θ的常用后验区间估计和最短后验区间估计,并对两者进行了对比,得出结论：在小样本情况下,最短可信区间计算方法值得采用。     

异方差下多个正态总体共同均值的参数bootstrap推断

文章使用参数bootstrap (PB)方法考虑了当方差未知且可以不相等时多个正态总体共同均值的假设检验和置信区间构造问题.基于共同均值一个著名估计,提出了一种参数bootstrap统计推断方法,并借助Mon-te Carlo方法与经典的近似解法和广义推断方法进行了比较.随机模拟结果表明,就第一类错误概率和覆盖率而言,参数bootstrap推断方法表现更好.参数bootstrap方法不仅具有满意的第一类错误概率和覆盖率,而且具有良好的检验功效和置信区间平均长度表现.     

两均匀分布区间长度比的置信区间与假设检验

文章在均匀分布区间长度的区间估计的基础上,给出两个均匀分布总体的区间长度比的估计量.并给出了它的置信区间和假设检验方法.     

Confidence Intervals for Survival Probabilities: A Comparison Study

The confidence interval of the Kaplan–Meier estimate of the survival probability at a fixed time point is often constructed by the Greenwood formula. This normal approximation-based method can be looked as a Wald type confidence interval for a binomial proportion, the survival probability, using the “effective” sample size defined by Cutler and Ederer. Wald-type binomial confidence interval has been shown to perform poorly comparing to other methods. We choose three methods of binomial confidence intervals for the construction of confidence interval for survival probability: Wilson's method, Agresti–Coull's method, and higher-order asymptotic likelihood method. The methods of “effective” sample size proposed by Peto et al. and Dorey and Korn are also considered. The Greenwood formula is far from satisfactory, while confidence intervals based on the three methods of binomial proportion using Cutler and Ederer's “effective” sample size have much better performance.     

Exact likelihood ratio and score confidence intervals for the binomial proportion

Many methods are available for computing a confidence interval for the binomial parameter, and these methods differ in their operating characteristics. It has been suggested in the literature that the use of the exact likelihood ratio (LR) confidence interval for the binomial proportion should be considered. This paper provides an evaluation of the operating characteristics of the two‐sided exact LR and exact score confidence intervals for the binomial proportion and compares these results to those for three other methods that also strictly maintain nominal coverage: Clopper‐Pearson, Blaker, and Casella. In addition, the operating characteristics of the two‐sided exact LR method and exact score method are compared with those of the corresponding asymptotic methods to investigate the adequacy of the asymptotic approximation. Copyright © 2013 John Wiley & Sons, Ltd.     

Performance of Interval Estimators for the Inverse Hypergeometric Distribution

The inverse hypergeometric distribution is of interest in applications of inverse sampling without replacement from a finite population where a binary observation is made on each sampling unit. Thus, sampling is performed by randomly choosing units sequentially one at a time until a specified number of one of the two types is selected for the sample. Assuming the total number of units in the population is known but the number of each type is not, we consider the problem of estimating this parameter. We use the Delta method to develop approximations for the variance of three parameter estimators. We then propose three large sample confidence intervals for the parameter. Based on these results, we selected a sampling of parameter values for the inverse hypergeometric distribution to empirically investigate performance of these estimators. We evaluate their performance in terms of expected probability of parameter coverage and confidence interval length calculated as means of possible outcomes weighted by the appropriate outcome probabilities for each parameter value considered. The unbiased estimator of the parameter is the preferred estimator relative to the maximum likelihood estimator and an estimator based on a negative binomial approximation, as evidenced by empirical estimates of closeness to the true parameter value. Confidence intervals based on the unbiased estimator tend to be shorter than the two competitors because of its relatively small variance but at a slight cost in terms of coverage probability.     

A GLM approach to step-stress accelerated life testing with interval censoring

In this paper, we present a statistical inference procedure for the step-stress accelerated life testing (SSALT) model with Weibull failure time distribution and interval censoring via the formulation of generalized linear model (GLM). The likelihood function of an interval censored SSALT is in general too complicated to obtain analytical results. However, by transforming the failure time to an exponential distribution and using a binomial random variable for failure counts occurred in inspection intervals, a GLM formulation with a complementary log-log link function can be constructed. The estimations of the regression coefficients used for the Weibull scale parameter are obtained through the iterative weighted least square (IWLS) method, and the shape parameter is updated by a direct maximum likelihood (ML) estimation. The confidence intervals for these parameters are estimated through bootstrapping. The application of the proposed GLM approach is demonstrated by an industrial example.     

End-Point Estimation for Decreasing Densities: Asymptotic Behaviour of the Penalized Likelihood Ratio

Abstract.  We consider the problem of estimating the modal value of a decreasing density on the positive real line. This has application in several interesting phenomena arising, for example, in renewal theory, and in biased and distance samplings. We use a penalized likelihood ratio-based approach for inference and derive the scale-free universal large sample null distribution of the log-likelihood ratio, using a suitably chosen penalty parameter. We present simulation results and a real data analysis to corroborate our findings, and compare the performance of the confidence sets with the existing results.     

An improved score interval with a modified midpoint for a binomial proportion

One of the most basic and important problems in statistical inference is the construction of the confidence interval (CI). In this paper, we propose a novel CI for a binomial proportion by modifying the midpoint of the score interval. The proposed modified interval can solve the ‘downward spikes’ problem of the score interval without enlarging the interval length. Simulation studies are carried out to illustrate the performance of the modified interval. With regard to the criterions of coverage probability, mean absolute error and expected length, our method is competitive among the several commonly used methods for constructing a CI. A real data example is also presented to show the application of our method.     

Confidence interval estimation for negative binomial group distribution

Negative binomial group distribution was proposed in the literature which was motivated by inverse sampling when considering group inspection: products are inspected group by group, and the number of non-conforming items of a group is recorded only until the inspection of the whole group is finished. The non-conforming probability p of the population is thus the parameter of interest. In this paper, the confidence interval construction for this parameter is investigated. The common normal approximation and exact method are applied. To overcome the drawbacks of these commonly used methods, a composite method that is based on the confidence intervals of the negative binomial distribution is proposed, which benefits from the relationship between negative binomial distribution and negative binomial group distribution. Simulation studies are carried out to examine the performances of our methods. A real data example is also presented to illustrate the application of our method.     

A procedure for approximate negative binomial tolerance intervals

In this article, we present a procedure for approximate negative binomial tolerance intervals. We utilize an approach that has been well-studied to approximate tolerance intervals for the binomial and Poisson settings, which is based on the confidence interval for the parameter in the respective distribution. A simulation study is performed to assess the coverage probabilities and expected widths of the tolerance intervals. The simulation study also compares eight different confidence interval approaches for the negative binomial proportions. We recommend using those in practice that perform the best based on our simulation results. The method is also illustrated using two real data examples.     

A comparative study of confidence intervals for negative binomial proportion

In this paper, we investigate four existing and three new confidence interval estimators for the negative binomial proportion (i.e., proportion under inverse/negative binomial sampling). An extensive and systematic comparative study among these confidence interval estimators through Monte Carlo simulations is presented. The performance of these confidence intervals are evaluated in terms of their coverage probabilities and expected interval widths. Our simulation studies suggest that the confidence interval estimator based on saddlepoint approximation is more appealing for large coverage levels (e.g., nominal level≤1% ) whereas the score confidence interval estimator is more desirable for those commonly used coverage levels (e.g., nominal level>1% ). We illustrate these confidence interval construction methods with a real data set from a maternal congenital heart disease study.     

The Empirical Likelihood Inference of a Regression Parameter in Censored Partial Linear Models Based on a Piecewise Polynomial

This article aims at making an empirical likelihood inference of regression parameter in partial linear model when the response variable is right censored randomly. The present studies are mainly designed to use empirical likelihood (EL) method based on synthetic dependent data, and the result cannot be applied directly due to the unknown weights in it. In this paper, we introduce a censored empirical log-likelihood ratio and demonstrate that its limiting distribution is a standard chi-square distribution. The estimating procedure of β is developed based on piecewise polynomial method. As a result, the p-value of test and the confidence interval can be obtained without estimating other quantities. Some simulation studies are conducted to highlight the performance of the proposed EL method, and the results show a good performance. Finally, we apply our method into the real example of multiple myeloma data and show the proof of theorem.