混合狄雷克利分布和高维表的贝叶斯估计

本文讨论多项分布情况下的高维列联表使用混合狄雷克利分布为先验分布时，贝叶斯估计的表达，以及独立性条件的表述．将文献［４］和［５］的结论推广到高维列联表中．     

线性模型参数向量的近似贝叶斯估计

用线性贝叶斯方法去同时估计线性模型中回归系数和误差方差,并在不知道先验分布具体形式的情况下,得到了线性贝叶斯估计的表达式.在均方误差矩阵准则下,证明了其优于最小二乘估计和极大似然估计.与利用MCMC算法得到的贝叶斯估计相比,线性贝叶斯估计具有显式表达式并且更方便使用.对于几种不同的先验分布,数值模拟结果表明线性贝叶斯估...     

基于分层贝叶斯广义线性模型的小域估计方法研究

当前,构建恰当的小域估计方法是解决我国政府抽样调查中多层次推断问题的关键所在。由于小域的小样本特性,基于频率统计学的小域估计方法推断效果并不理想,而传统基于贝叶斯统计视角的小域估计方法在非连续型变量估计时适应性不强。本文在系统介绍传统贝叶斯小域估计方法的基站上,为了解决离散变量的估计推断问题,将广义线性模型引入到分层贝叶斯方法中,构建了基本的理论机制和分类数据的估计模型。基于此模型,运用全国流动人口动态监测调查2014年广东省内的样本数据进行实例测算,估计出广东省各地级市的流动人口学历分布情况,并将分层贝叶斯广义线性模型的估计结果与传统估计方法进行了对比分析。结果显示,分层贝叶斯广义线性模型在样本量充足的情况下能够准确地估计出目标小域的总体参数,在样本量不足的小域中依然能够给出稳健的估计结果。文章所构建的估计模型不仅可以充分利用先验信息和辅助信息,还适用于对复杂数据进行估计推断,能够为我国政府抽样调查的小域估计实践提供有价值的理论参考。     

线性指数分布的简单贝叶斯估计[英文]

本文利用Kaminskiy和Vasiliy提出的简单贝叶斯估计过程,研究线性指数分布的参数的简单贝叶斯估计.本文的创新之处是利用了核密度估计法和缺一交叉验证法构造概率密度函数.在估计过程中,先验信息可以通过可靠度函数估计的区间形式表示.基于这种先验信息,可以构造线性指数分布参数的连续联合先验分布,并可以给出在任意给定时刻可靠度函数的均值及标准差的后验估计.通过一个数值例子说明这种估计方法.Rayleigh分布是线性指数分布的特殊情况,通过简单贝叶斯估计过程,给出了Rayleigh分布的尺度参数的一种新的先验分布,这个模型的均值可由一个级数逼近.     

Linex损失下对数伽玛分布的经验贝叶斯估计的收敛速度

在Linex损失函数下,研究对数伽玛分布的经验贝叶斯估计问题,在适当的条件下,得到了经验贝叶斯估计的收敛速度.最后给出一个例子,说明定理的合理性.     

企业诚信度的贝叶斯估计磷究

本文利用贝叶斯分析方法建立了评估企业诚信度的概率估计模型，并选取了一些有代表性的企业进行实证分析。与现有的同类问题研究相比，本模型的特点是将决策者个人经验和主观判断作为先验信息与样本信息相结合、将财务数据与诚信表现相结合，从而提高了估计的可靠性和准确性。     

企业诚信度的贝叶斯估计研究

本文利用贝叶斯分析方法建立了评估企业诚信度的概率估计模型，并选取了一些有代表性的企业进行实证分析。与现有的同类问题研究相比，本模型的特点是将决策者个人经验和主观判断作为先验信息与样本信息相结合、将财务数据与诚信表现相结合，从而提高了估计的可靠性和准确性。     

多元线性回归中回归系数的线性经验Bayes估计

本文构造了多元线性回归模型回归系数向量的线性经验Bayes估计,并在较一般的条件下得到了渐近最优的收敛速度。     

污染数据情形下线性指数分布参数的经验贝叶斯估计（英文）

本文研究污染数据情形下线性指数分布参数的经验贝叶斯估计问题.在平方损失函数下,导出参数的贝叶斯估计以及利用解卷积的核方法构造该参数的经验贝叶斯估计.在合适的条件下,得到基于超平滑误差分布类所提出的经验贝叶斯估计的一致收敛速度.     

Testing for Ordered Trends of Binary Responses between Contingency Tables

In this article, likelihood ratio tests (LRTs) are developed for detecting that stochastic trends of binary responses are ordered between 2×k contingency tables. We provide a simple iterative algorithm for the maximum likelihood estimators under the order restriction and construct the LRTs using those estimators. All the distributional results of these tests are based on the large sampling theory. The finite-sample behaviors of these tests are investigated through a simulation study. As an illustration of these tests, we analyze a set of data on wheeziness of smoking coalminers.     

Exact Cumulant Calculations for Pearson X2 and Zelterman Statistics for r-Way Contingency Tables

Abstract

We present a computer algebra procedure that calculates exact cumulants for Pearson X ² and Zelterman statistics for r-way contingency tables. The algorithm is an example of how an overwhelming algebraic problem can be solved neatly through computer implementation by emulating tactics that one uses by hand. For inference purposes the cumulants may be used to assess chi-square approximations or to improve this approximation via Edgeworth expansions. Edgeworth approximations are compared to the computerintensive techniques of Mehta and Patel that provide exact and arbitrarily close results. Comparisons to approximations that utilize the gamma distribution (Mielke and Berry) are also made.     

Lp-Lq Estimates for a Linear Perturbed Klein-Gordon Equation

We consider L^p-L^q estimates for the solution u(t,x) to tbe following perturbed Klein-Gordon equation ∂_{tt}u - Δu + u + V(x)u = 0 \qquad x∈ R^n, n ≥ 3 u(x,0) = 0, ∂_tu(x,0) = f(x) We assume that the potential V(x) and the initial data f(x) are compact, and V(x) is sufficiently small, then the solution u(t,x) of the above problem satisfies ||u(t)||_q ≤ Ct^{-a}||f||_p for t > 1 where a is the piecewise-linear function of 1/p and 1/q.     

A Stochastic Approach to Error Estimates for Iterative Linear Solvers: Part 1

We consider the problem of estimating the magnitude of the error of an iterative linear solver after k iterations. Assuming that the initial error can be described using a probability distribution we derive L2-estimates for the magnitude of the error in the average case. In Part 1 the ideas are presented and applied to a simple splitting method, while Part 2 extends the same ideas to the conjugate gradient method.This revised version was published online in October 2005 with corrections to the Cover Date.     

Sampling for Conditional Inference on Contingency Tables

We propose new sequential importance sampling methods for sampling contingency tables with given margins. The proposal for each method is based on asymptotic approximations to the number of tables with fixed margins. These methods generate tables that are very close to the uniform distribution. The tables, along with their importance weights, can be used to approximate the null distribution of test statistics and calculate the total number of tables. We apply the methods to a number of examples and demonstrate an improvement over other methods in a variety of real problems. Supplementary materials are available online.     

Relevance of the Bayesian paradigm for “applied probabilists”

This paper is based on an invited lecture given by the author at the ORSA/TIMS Special Interest Group on Applied Probability Conference onStatistical and Computational Problems in Probability Modeling, held at Williamsburg, Virginia, January 7–9, 1985.The theme of this paper is twofold. First, that members of the above group should be seriously concerned with issues of statistical inference — they should not stop short upon proposing a probability model. Second, that inference be undertaken via a strict adherence to the rules of probability — the Bayesian paradigm. To underscore a need for emphasizing the first theme, it may be pertinent to note that an overwhelming majority of the papers dealing with statistical and inferential issues that were presented at this conference were authored by members who did not claim to belong to the ORSA/TIMS Special Interest Group on Applied Probability.The lecture was followed by a panel discussion, with Drs. Lyle Broemeling and Edward Wegman of the Office of Naval Research as discussants. Dr. Robert Launer of the Army Research Office served as a moderator. Discussions from the floor included comments by Professors D. Harrington of Harvard University, E. Parzen of Texas A & M University, and R. Smith of Imperial College, London, England. This paper, and the comments of the panelists, are published in this volume of theAnnals of Operations Research, which is going to serve as a Proceedings of the Conference.Supported by Contract No. N00014-85-K-0202, Office of Naval Research, and Grant No. DAAG 29-84-K-0160, Army Research Office.     

线性回归估计相合性问题的新进展

本文综述了近年来线性回归最小二乘仗霜合性问题的一些新进展，对在误差方差无限的情况下这种估计的特殊表现作了论述，并提出了若干尚未解决的问题。     

Error Estimates for the Approximation of a Class of Optimal Control Systems Governed by Linear PDEs

This paper deals with a class of optimal control problems in which the system is governed by a linear partial differential equation and the control is distributed and with constraints. The problem is posed in the framework of the theory of optimal control of systems. A numerical method is proposed to approximate the optimal control. In this method, the state space as well as the convex set of admissible controls are discretized. An abstract error estimate for the optimal control problem is obtained that depends on both the approximation of the state equation and the space of controls. This theoretical result is illustrated by some numerical examples from the literature.     

多维广义线性模型拟极大似然估计的弱相合性

本文考虑多维广义线性模型的拟似然方程$\tsm^n_{i=1}X_i(y_i-\mu(X_i'\xb))=0$, 在一定条件下证明了此方程的解$\wh\xb_n$渐近存在, 并得到了其收敛速度, 即$\wh\xb_n-\xb_0=O_p({\underline{\xl}}_n^{-1/2})$, 其中$\xb_0$为参数$\xb$的真值, $\underline{\xl}_n$是方阵$S_n=\tsm^n_{i=1}X_iX_i'$的最小特征值。