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.     

双参数指数分布尺度参数的区间估计

基于残缺的样本观测数据,文章讨论了双参数指数分布总体尺度参数的区间估计问题.给出了适用于残缺观测数据的构造置信区间的一种新方法,讨论了枢轴量的精确分布和大样本近似分布,得到了尺度参数的近似置信区间.这个结果还适用于样本中可能存在异常数据的情形,具有稳健性.     

通货膨胀与股价、房地产价格、货币供给量的相关性研究

文章建立了向量自回归模型,在估计无约束向量自回归参数的基础上,通过脉冲响应函数、方差分解,探讨了通货膨胀、房地产价格指数、股票价格指数和货币供给量的动态关系。     

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。     

系统抽样、脉冲响应与通货膨胀惯性

利用时间序列模型研究经济变量的动态特征时,模型估计结果通常随样本观测频率改变而变化。本文针对系统抽样问题,研究样本观测频率变化对脉冲响应函数和累积脉冲响应的影响,探讨不同观测频率下脉冲响应函数的理论联系,以及对变量持久性特征更加稳健的度量方法,在此基础上考察中国通货膨胀的动态特征。结果表明,累积脉冲响应和惯性系数随样本观测频率提高而增加;系统抽样对脉冲响应函数的影响源于外生冲击界定的不同,脉冲响应的数值意义有限,但可用其考察冲击影响的持续时间;外生冲击对中国通货膨胀的影响持续2年左右,且90%在1年内实现,央行要对通胀走势有很强的预判能力,政策滞后将增加反通胀成本。     

独立逆抽样下优势比的置信区间

本文对独立逆抽样设计下优势比的置信区间的构造进行了研究,包括三个已有的方法,以及本文引入的鞍点逼近方法。通过模拟比较了这四个方法给出的置信区间。模拟结果表明,基于鞍点逼近方法给出的置信区间不比另外三种方法差。并且在一些情况下表现还优于其它三个方法。     

不完全数据场合下双参数指数分布参数的区间估计

文章讨论了双参数指数分布参数基于不完全数据情况下的置信区间的构造问题.针对门限参数和尺度参数,分别给出了用于构造置信区间的枢轴量,讨论了门限参数的枢轴量以及尺度参数的枢轴量的精确分布,得到了相应的置信区间.针对尺度参数置信区间构造的枢轴量可以抵抗样本中异常数据的干扰,具有一定程度的稳健性.     

关于捕获再捕获抽样的置信区间

捕获再捕获抽样是一种应用广泛的抽样方法。运用随机模拟,研究捕获再捕获抽样的三种估计量的均值、方差、偏度、峰度、利用近似正态分布构造的置信区间的统计性质。改进了估计量的样本方差计算公式,使得利用近似正态分布构造的置信区间更优。     

政府支出对经济增长的动态冲击效应分析

文章分别建立二元和三元结构向量自回归(SVAR)模型,运用脉冲响应函数和方差分解分析,实证检验了市级地方政府支出对市区经济增长的动态冲击效应。     

从海权角度看航运金融对我国经济发展的影响

目前,国内有很多文献研究到了航运、金融发展与经济增长之间的关系,比如,王海俊利用协整模型和脉冲响应函数分析了港口物流与经济发展之间的关系,证明了港口物流可以拉动经济增长。谢太峰、王子博构建误差修正模型并运用Granger因果检验模型对上海区域金融发展与区域经济增长的内在关系进行实证研究,得出结论：上海区域金融发展对区域经济增长具有正向带动作用。张明香采用向量自回归（VAR）模型、向量误差修正模型、脉冲响应函数和方差分解等动态计量方法分析港口对区域经济的短期影响效应,     

Simultaneous Inferences on the Cumulative Distribution Function of a Normal Distribution

This paper addresses the problem of constructing simultaneous confidence intervals for the cumulative distribution function of a normal distribution at several specified points. The procedure is based upon the observation of a random sample of independent observations from a normal distribution with an unknown mean and variance. A new methodology is proposed for obtaining confidence intervals with a specified overall simultaneous confidence level through the inversion of acceptance sets. Both one-sided and two-sided confidence intervals are considered. Some illustrations of the new method are provided, and comparisons are made with other approaches to the problem.     

Confidence Intervals on Regression Models with Censored Data

Stute (1993, Consistent estimation under random censorship when covariables are present. Journal of Multivariate Analysis 45, 89–103) proposed a new method to estimate regression models with a censored response variable using least squares and showed the consistency and asymptotic normality for his estimator. This article proposes a new bootstrap-based methodology that improves the performance of the asymptotic interval estimation for the small sample size case. Therefore, we compare the behavior of Stute's asymptotic confidence interval with that of several confidence intervals that are based on resampling bootstrap techniques. In order to build these confidence intervals, we propose a new bootstrap resampling method that has been adapted for the case of censored regression models. We use simulations to study the improvement the performance of the proposed bootstrap-based confidence intervals show when compared to the asymptotic proposal. Simulation results indicate that, for the new proposals, coverage percentages are closer to the nominal values and, in addition, intervals are narrower.     

Confidence intervals for extreme Pareto-type quantiles

In this paper, we revisit the construction of confidence intervals for extreme quantiles of Pareto-type distributions. A novel asymptotic pivotal quantity is proposed for these quantile estimators, which leads to new asymptotic confidence intervals that exhibit more accurate coverage probability. This pivotal quantity also allows for the construction of a saddle-point approximation, from which a second set of new confidence intervals follows. The small-sample properties and utility of these confidence intervals are studied using simulations and a case study from insurance.     

Simultaneous Confidence Intervals for the Population Cell Means,for Two-by-Two Factorial Data,that Utilize Uncertain Prior Information

Consider a two-by-two factorial experiment with more than one replicate. Suppose that we have uncertain prior information that the two-factor interaction is zero. We describe new simultaneous frequentist confidence intervals for the four population cell means, with simultaneous confidence coefficient 1 ? α, that utilize this prior information in the following sense. These simultaneous confidence intervals define a cube with expected volume that (a) is relatively small when the two-factor interaction is zero and (b) has maximum value that is not too large. Also, these intervals coincide with the standard simultaneous confidence intervals obtained by Tukey’s method, with simultaneous confidence coefficient 1 ? α, when the data strongly contradict the prior information that the two-factor interaction is zero. We illustrate the application of these new simultaneous confidence intervals to a real data set.     

Nonparametric confidence intervals for Tmax in sequence-stratified crossover studies

Tmax is the time associated with the maximum serum or plasma drug concentration achieved following a dose. While Tmax is continuous in theory, it is usually discrete in practice because it is equated to a nominal sampling time in the noncompartmental pharmacokinetics approach. For a 2-treatment crossover design, a Hodges-Lehmann method exists for a confidence interval on treatment differences. For appropriately designed crossover studies with more than two treatments, a new median-scaling method is proposed to obtain estimates and confidence intervals for treatment effects. A simulation study was done comparing this new method with two previously described rank-based nonparametric methods, a stratified ranks method and a signed ranks method due to Ohrvik. The Normal theory, a nonparametric confidence interval approach without adjustment for periods, and a nonparametric bootstrap method were also compared. Results show that less dense sampling and period effects cause increases in confidence interval length. The Normal theory method can be liberal (i.e. less than nominal coverage) if there is a true treatment effect. The nonparametric methods tend to be conservative with regard to coverage probability and among them the median-scaling method is least conservative and has shortest confidence intervals. The stratified ranks method was the most conservative and had very long confidence intervals. The bootstrap method was generally less conservative than the median-scaling method, but it tended to have longer confidence intervals. Overall, the median-scaling method had the best combination of coverage and confidence interval length. All methods performed adequately with respect to bias.     

Conservative confidence intervals for a single parameter

The method of constructing confidence intervals from hypothesis tests is studied in the case in which there is a single unknown parameter and is proved to provide confidence intervals with coverage probability that is at least the nominal level. The confidence intervals obtained by the method in several different contexts are seen to compare favorably with confidence intervals obtained by traditional methods. The traditional intervals are seen to have coverage probability less than the nominal level in several instances, This method can be applied to all confidence interval problems and reduces to the traditional method when an exact pivotal statistic is known.     

Simultaneous Two-Sided Tolerance Intervals for a Univariate Linear Regression Model

In this article we deal with simultaneous two-sided tolerance intervals for a univariate linear regression model with independent normally distributed errors. We present a method for determining the intervals derived by the general confidence-set approach (GCSA), i.e. the intervals are constructed based on a specified confidence set for unknown parameters of the model. The confidence set used in the new method is formed based on a suggested hypothesis test about all parameters of the model. The simultaneous two-sided tolerance intervals determined by the presented method are found to be efficient and fast to compute based on a preliminary numerical comparison of all the existing methods based on GCSA.     

Interval estimation via tail functions

The authors describe a new method for constructing confidence intervals. Their idea consists in specifying the cutoff points in terms of a function of the target parameter rather than as constants. When it is suitably chosen, this so‐called tail function yields shorter confidence intervals in the presence of prior information. It can also be used to improve the coverage properties of approximate confidence intervals. The authors illustrate their technique by application to interval estimation of the mean of Bernoulli and normal populations. They further suggest guidelines for choosing the optimal tail function and discuss the relationship with Bayesian inference.     

Estimating DEA confidence intervals with statistical panel data analysis

This paper describes a statistical method for estimating data envelopment analysis (DEA) score confidence intervals for individual organizations or other entities. This method applies statistical panel data analysis, which provides proven and powerful methodologies for diagnostic testing and for estimation of confidence intervals. DEA scores are tested for violations of the standard statistical assumptions including contemporaneous correlation, serial correlation, heteroskedasticity and the absence of a normal distribution. Generalized least squares statistical models are used to adjust for violations that are present and to estimate valid confidence intervals within which the true efficiency of each individual decision-making unit occurs. This method is illustrated with two sets of panel data, one from large US urban transit systems and the other from a group of US hospital pharmacies.     

Assessing case influence on confidence intervals in nonlinear regression

Diagnostic techniques are proposed for assessing the influence of individual cases on confidence intervals in nonlinear regression. The technique proposed uses the method of profile t-plots applied to the case-deletion model. The effect of the geometry of the statistical model on the influence measures is assessed, and an algorithm for computing case-deleted confidence intervals is described. This algorithm provides a direct method for constructing a simple diagnostic measure based on the ratio of the lengths of confidence intervals. The generalization of these methods to multiresponse models is discussed.