Likelihood Based Confidence Intervals for the Tail Index

For the estimation of the tail index of a heavy tailed distribution, one of the well-known estimators is the Hill estimator (Hill, 1975). One obvious way to construct a confidence interval for the tail index is via the normal approximation of the Hill estimator. In this paper we apply both the empirical likelihood method and the parametric likelihood method to obtaining confidence intervals for the tail index. Our limited simulation study indicates that the normal approximation method is worse than the other two methods in terms of coverage probability, and the empirical likelihood method and the parametric likelihood method are comparable.     

混合线性模型中方差比的置信区间

本文利用信仰推断给出了求混合线性模型中方差比的置信区间的方法，结果表明所得区间估计具有不变性。另外，本文给出了两种求区间估计的近似方法。     

On estimation of the extended Orey index for Gaussian processes

Orey suggested the definition of an index for a Gaussian process with stationary increments which determines various properties of the sample paths of this process. We provide an extension of the definition of the Orey index towards a second-order stochastic process which may not have stationary increments and estimate the Orey index towards a Gaussian process from discrete observations of its sample paths.     

Exact L 2 Small Balls of Gaussian Processes

We prove a comparison theorem extending Li(6) and develop a complex-analytic approach to treat L ² small ball probabilities of Gaussian processes. We demonstrate the techniques for the m-times integrated Brownian motions and in examples where one can not apply Li comparison theorem.     

Cycle Range Distributions for Gaussian Processes Exact and Approximative Results

Wave cycles, i.e. pairs of local maxima and minima, play an important role in many engineering fields. Many cycle definitions are used for specific purposes, such as crest–trough cycles in wave studies in ocean engineering and rainflow cycles for fatigue life predicition in mechanical engineering. The simplest cycle, that of a pair of local maximum and the following local minimum is also of interest as a basis for the study of more complicated cycles. This paper presents and illustrates modern computational tools for the analysis of different cycle distributions for stationary Gaussian processes with general spectrum. It is shown that numerically exact but slow methods will produce distributions in almost complete agreement with simulated data, but also that approximate and quick methods work well in most cases. Of special interest is the dependence relation between the cycle average and the cycle range for the simple maximum–minimum cycle and its implication for the range distribution. It is observed that for a Gaussian process with rectangular box spectrum, these quantities are almost independent and that the range is not far from a Rayleigh distribution. It will also be shown that had there been a Gaussian process where exact independence hold then the range would have had an exact Rayleigh distribution. Unfortunately no such Gaussian process exists.This revised version was published online in March 2005 with corrections to the cover date.     

基于置信分布的多个对数正态总体的公共均值的置信区间的构建（英文）

在实际应用中需要拟合正的偏态数据时,对数正态分布是通常的选择.当通过多重比较确定了多个对数正态分布总体的均值相同时,如何能够利用更多的信息,同时使用这些对数正态分布总体的信息来构建公共均值的置信区间成为了众多学者颇为关注的问题.本篇文章提出了一种新的基于置信分布的方法来构建多个对数正态总体公共均值的置信区间,该方法通过对相关量的样本方差进行加权来提高效率.进而对文中提出的基于置信分布的置信区间的构建方法进行了蒙特卡洛模拟研究,模拟结果表明,我们提出的构建方法可以得到很好的覆盖概率和较短的区间宽度.文章的最后用三个实际数据来验证了文中所提出方法的表现.     

�������ŷֲ��Ķ��������̬����Ĺ�����ֵ����������Ĺ���

The log-normal distribution is a common choice for modeling positively skewed data arising from many practical applications.This article introduces a new method of constructing confidence interval for a common mean shared by several log-normal populations through confidence distributions, which combines all information from independent sources. We develop a non-trivial weighting approach by taking account of the sample variances of related quantities to enhance efficiency. Combined confidence distributions are used to construct confidence intervals for the common mean and a simplified version of one existing method is also proposed. We conduct simulation studies to evaluate the performance of the proposed methods in comparison with existing methods. Our simulation results show that the weighting approach yields shorter interval length than the non-weighting approach. The newly proposed confidence intervals perform very well in terms of empirical coverage probability and average interval length. Finally, applications of the proposed methodology is illustrated through three real data examples.     

Two-Stage Procedure of Fixed-Width Confidence Intervals for the Risk Ratio

Methodology and Computing in Applied Probability - A two-stage procedure is considered for obtaining fixed-width confidence intervals and optimal sample sizes for the risk ratio of two independent...     

正态总体中方差的两步置信区间

利用两阶段抽样，我们具体给出了正态总体方差的一个置信区间，它同时满足可靠度与精度。利用数值计算的方法，我们计算出第一阶段的最优抽样量。此外，我们证明了一次抽样时同时满足可靠度与精度的置信区间的不存在性。     

Confidence Intervals for Discrete Approximations to Ill-Posed Problems

We consider the linear model Y = Xβ + ε that is obtained by discretizing a system of first-kind integral equations describing a set of physical measurements. The n vector β represents the desired quantities, the m x n matrix X represents the instrument response functions, and the m vector Y contains the measurements actually obtained. These measurements are corrupted by random measuring errors ε drawn from a distribution with zero mean vector and known variance matrix. Solution of first-kind integral equations is an ill-posed problem, so the least squares solution for the above model is a highly unstable function of the measurements, and the classical confidence intervals for the solution are too wide to be useful. The solution can often be stabilized by imposing physically motivated nonnegativity constraints. In a previous article (O'Leary and Rust 1986) we developed a method for computing sets of nonnegatively constrained simultaneous confidence intervals. In this article we briefly review the simultaneous intervals and then show how to compute nonnegativity constrained one-at-a-time confidence intervals. The technique gives valid confidence intervals even for problems with m < n. We demonstrate the methods using both an overdetermined and an underdetermined problem obtained by discretizing an equation of Phillips (Phillips 1962).     

Confidence Intervals for Quantiles and Tolerance Intervals Based on Ordered Ranked Set Samples

Confidence intervals for quantiles and tolerance intervals based on ordered ranked set samples (ORSS) are discussed in this paper. For this purpose, we first derive the cdf of ORSS and the joint pdf of any two ORSS. In addition, we obtain the pdf and cdf of the difference of two ORSS, viz. , 1 ≤ r < s ≤ N. Then, confidence intervals for quantiles based on ORSS are derived and their properties are discussed. We compare with approximate confidence intervals for quantiles given by Chen (Journal of Statistical Planning and Inference, 83, 125–135; 2000), and show that these approximate confidence intervals are not very accurate. However, when the number of cycles in the RSS increases, these approximate confidence intervals become accurate even for small sample sizes. We also compare with intervals based on usual order statistics and find that the confidence interval based on ORSS becomes considerably narrower than the one based on usual order statistics when n becomes large. By using the cdf of, we then obtain tolerance intervals, discuss their properties, and present some tables for two-sided tolerance intervals.     

Lincoln-Petersen模型中群体大小的置信限和置信区间

对于Lincoln-Petersen模型,基于捕获-再捕获方法得到的数据,我们给出了群体大小的置信限和置信区间的有效计算方法.     

有序处理连续比较的置信区间

为有序均值的连续比较提出了一个新的检验过程.他们的过程对满足简单序的均值有重要的应用价值,例如在研究增长剂量对药物效用的影响. 与其它检验过程相比(例:Hayter\ucite{4}中的检验), 其优点在于产生了更短的连续比较的置信限,从而能够提供更多机会发现在何药剂量处有不同的效用.但作为有序均值的齐性检验, 它的势表现远劣于其它检验.本文的目的是提出一检验过程在尽量保持Lee andSpurrier\ucite{8}检验的优点的同时大大地提高其势表现.     

Fast and Exact Simulation of Complex-Valued Stationary Gaussian Processes Through Embedding Circulant Matrix

This article is concerned with the study of the embedding circulant matrix method to simulate stationary complex-valued Gaussian sequences. The method is, in particular, shown to be well-suited to generate circularly symmetric stationary Gaussian processes. We provide simple conditions on the complex covariance function ensuring the theoretical validity of the minimal embedding circulant matrix method. We show that these conditions are satisfied by many examples and illustrate the simulation algorithm. In particular, we present a simulation study involving the circularly symmetric fractional Gaussian noise, a model introduced in this article. Supplementary material for this article is available online.     

Exact L 2-Small Ball Asymptotics of Gaussian Processes and the Spectrum of Boundary-Value Problems

We sharpen a classical result on the spectral asymptotics of boundary-value problems for self-adjoint ordinary differential operator. Using this result, we obtain the exact L ₂-small ball asymptotics for a new class of zero-mean Gaussian processes. This class includes, in particular, the integrated generalized Slepian process, integrated centered Wiener process, and integrated centered Brownian bridge. Partially supported by RFFR grant No.07-01-00159 and by grant NSh-227.2008.1.     

Fast Bootstrap Confidence Intervals for Continuous Threshold Linear Regression

Continuous threshold regression is a common type of nonlinear regression that is attractive to many practitioners for its easy interpretability. More widespread adoption of threshold regression faces two challenges: (i) the computational complexity of fitting threshold regression models and (ii) obtaining correct coverage of confidence intervals under model misspecification. Both challenges result from the nonsmooth and nonconvex nature of the threshold regression model likelihood function. In this article we first show that these two issues together make the ideal approach for making model-robust inference in continuous threshold linear regression an impractical one. The need for a faster way of fitting continuous threshold linear models motivated us to develop a fast grid search method. The new method, based on the simple yet powerful dynamic programming principle, improves the performance by several orders of magnitude. Supplementary materials for this article are available online.     

Confidence Interval for the Risk-Score Index

Risk-score indices are a simple, applicable, and easy-to-calculate tool for regression models, which can be used when computers are not available. The risk-score index is a partial summation of the rounded model coefficients that maintains essential properties of the model coefficients, such as their weight in the model and the correlation matrix. In a certain sense, the risk score can be viewed as a transformation into a pre-selected scale. The risk score is generally represented as a point estimate. Thus, the risk-score index is a categorical variable that relates each and every category uniquely to risk. The main argument of this paper is that the risk score, which is calculated as a partial summation of rounded beta coefficients, is a statistic, and, therefore, it has its own variance. This variance is divided into three factors—the original β-coefficients variances, the rounding-error variance, and the variance from the relations between these two factors. Since the variance of the score is typically not negligible, it is preferable to consider the confidence interval centered at the point estimate and not just the point estimate itself. By using the confidence interval for the risk score, one can quantify the accuracy of the score and also compare different scores, which otherwise is not always possible.     

�����Ͳ��ɷֲ������Ĺ̶�����������乹�췽��

Interval width and coverage probability are two criteria for evaluating confidence intervals. It's quite worthwhile to investigate fixed-width confidence intervals with a prescribed nominal level, which, in generally speaking, is hardly realized in fixed-sample-size circumstances. A common way to deal with this problem is to apply sequential methods and two-stage sampling or even multi-stage sampling. For zero-inflated Poisson distribution with a probability mass $p$ and Poissonmean parameter $lambda$, the construction of fixed-width confidence intervals for (lambda,p)$ is conducted in this paper, including sequentialand two-stage procedures. Each procedure is demonstrated to satisfy asymptotic consistency and efficiency. The variation of optimal fixed-samplesize by the two parameters is considered under different situations and simulation performance is displayed by Monte Carlo simulation. A real dataanalysis is also implemented for application.     

多个偏正态总体共同位置参数的Bootstrap置信区间

该文基于Bootstrap 方法研究多个偏正态总体共同位置参数的区间估计和假设检验问题.首先,分别给出未知参数的矩估计和极大似然估计.其次,将徐礼文[1]对多个正态总体共同均值的探讨推广到多个偏正态总体,进而构造共同位置参数的Bootstrap 置信区间和Bootstrap检验统计量.Monte Carlo模拟结果表明...     

Logistic响应分布分位数的鞍点近似置信区间

论文基于响应数据，应用鞍点近似方法，给出构造Logistic响应分布分位数的近似置信区间的方法. 论文还对这种置信区间进行了模拟，并将该方法应用于QD8电雷管. 模拟和实例结果表明，当样本量较小时，该方法能够较好地推断Logistic响应分布的分位数