后验分布在贝叶斯统计课堂教学的延伸

本文由贝叶斯公式的事件形式和贝叶斯公式的密度函数形式(分布律形式)引出后验分布,也即是由先验信息和似然函数得到后验分布,并运用探究性教学法考虑能否由后验分布得到先验分布.文献[1-3]指出已知似然函数和后验分布也能得到先验分布,并通过例题验证.     

多服务台可修排队的稳态分布存在条件

本文分析多服务台可修排队系统的稳态分布存在条件。多服务台可修排队系统可利用拟生灭过程理论处理。拟生灭过程方法给出了矩阵形式的多服务台可修排队系统的稳态分布存在条件。本文由这一矩阵形式的稳态分布存在条件导出具有明显概率意义的稳态分布存在条件的另一种形式，从而证明了两种不同形式的稳态分布存在条件的一致性。     

MCMC样本确定的后验密度的收敛性

本文讨论用MCMC样本确定的缺失数据的后验分布收敛到精确分布的问题，给出了几种度量形式下的收敛性。并严格证明了经验分布的后验形式的几个极限定理。     

位置参数分布族中Fiducial分布与后验分布的关系

本文考虑本质位置参数分布族中,参数的Fiducial分布与后验分布的等同问题．首先讨论了如何给出Fiducial分布,分析结果表明以分布函数形式给出Fiducial分布要比密度函数形式合理,同时,证明了所给的Fiducial分布具有频率性质．然后,研究在参数受到单侧限制时,Fiducial分布与后验分布等同的问题,给出的充要条件是分布族为指数分布族,此时,先验分布是一个广义先验分布,它不能被Lebesgue测度控制．最后,证明了在参数限制在一个有限区间内时,Fiducial分布与任何先验(包括广义先验分布)下的后验分布不等同．     

位置参数分布族中Fiducial分布与后验分布的关系

本文考虑本质位置参数分布族中,参数的Fiducial分布与后验分布的等同问题.首先讨论了如何给出Fiducial分布,分析结果表明以分布函数形式给出Fiducial分布要比密度函数形式合理,同时,证明了所给的Fiducial分布具有频率性质.然后,研究在参数受到单侧限制时,Fiducial分布与后验分布等同的问题,给出的充要条件是分布族为指数分布族,此时,先验分布是一个广义先验分布,它不能被Lebesgue测度控制.最后,证明了在参数限制在一个有限区间内时,Fiducial分布与任何先验(包括广义先验分布)下的后验分布不等同.     

线性函数关系的最大似然估计与似然比检验——关于左(n)不变分布的场合

本文在假定误差分布是左(n)不变的条件下,导出了关于多元线性模型的均值向量之间的线性关系存在性的似然比检验,以及相应参数的最大似然估计量。这些统计量的形式与分布函数的形式无关,因而是稳健的。     

两两NQD序列线性形式的强稳定性

本文研究了两两NQD序列线性形式的强稳定性, 得到了不同分布两两NQD序列具有线性形式强稳定性的充分条件.     

用于指数可靠性增长模型的一类新的先验分布

该文提出了可用于指数分布产品四种可靠性增长试验方案的一类新的先验分布. 这类先验分布以条件分布形式给出, 它适合可靠性增长试验中的各种情况. 各阶段的条件均值和条件方差的表达式被获得, 先验分布的形式与它们的参数间的关系被讨论. 这些结果有助于与专家意见相结合.本文还给出试验末尾产品可靠性的后验密度, Bayesian估计和Bayesian下限.     

EC_n(μ,I_n,φ)下的两种具体广义非中心X~2分布

借助于超几何函数,在广义非中心X~2分布级数形式密度函数表达式的基础上列出了两类具体椭球等高分布下的广义非中心X~2分布密度函数的精确表达,并给出了详细的证明过程;同时计算了这两类具体椭球等高分布下的广义非中心X~2分布对应高阶矩的形式,作为推论验证了非中心X~2分布相关的结论.     

基于MCMC的贝叶斯变结构金融时序GARCH模型研究

针对变结构GARCH模型没有解析形式的条件后验分布的问题。借助辅助变量把没有具体解析形式的后验分布转化为一系列完全条件分布,实现了变结构GARCH模型参数的贝叶斯估计。中国外汇市场波动性的实证研究,表明了辅助变量-Gibbs抽样有效的解决了贝叶斯变结构GARCH模型中的高维数值计算问题,并发现其波动持续性是由时间序列的状态转移引起的。     

Likelihood inferences for the link function without knowing the true underlying distributions

This article is concerned with inference about link function in generalized linear models. A parametric and yet robust likelihood approach is introduced to accomplish the intended goal. More specifically, it is demonstrated that one can convert normal and gamma likelihoods into robust likelihood functions for the link function. The asymptotic validity of the robust likelihood requires only the existence of the second moments of the underlying distributions. The application of this novel robust likelihood method is demonstrated on the Box–Cox transformation. Simulation studies and real data analysis are provided to demonstrate the efficacy of the new parametric robust procedures.     

Empirical Likelihood Semiparametric Regression Analysis under Random Censorship

This paper considers large sample inference for the regression parameter in a partly linear model for right censored data. We introduce an estimated empirical likelihood for the regression parameter and show that its limiting distribution is a mixture of central chi-squared distributions. A Monte Carlo method is proposed to approximate the limiting distribution. This enables one to make empirical likelihood-based inference for the regression parameter. We also develop an adjusted empirical likelihood method which only appeals to standard chi-square tables. Finite sample performance of the proposed methods is illustrated in a simulation study.     

随机插补下两线性模型中响应变量分位数差异的经验似然置信区间

设两个样本数据不完全的线性模型,其中协变量的观测值不缺失,响应变量的观测值随机缺失。采用随机回归插补法对响应变量的缺失值进行补足,得到两个线性回归模型的"完全"样本数据,在一定条件下得到两响应变量分位数差异的对数经验似然比统计量的极限分布为加权x_1~2,并利用此结果构造分位数差异的经验似然置信区间。模拟结果表明在随机插补下得到的置信区间具有较高的覆盖精度。     

重复测量试验模型参数似然比检验及其功效分析

本文给出了在重复测量试验模型下, 当受试对象观测向量的协方差矩阵$\Sigma$为复合对称阵时,参数的似然比检验统计量; 给出该检验在原假设下的渐近零分布和在备择假设下的渐近非零分布;并就其功效进行了分析.     

Noncentral matrix quadratic forms of the skew elliptical variables

In this paper, the noncentral matrix quadratic forms of the skew elliptical variables are studied. A family of the matrix variate noncentral generalized Dirichlet distributions is introduced as the extension of the noncentral Wishart distributions, the Dirichlet distributions and the noncentral generalized Dirichlet distributions. Main distributional properties are investigated. These include probability density and closure property under linear transformation and marginalization, the joint distribution of the sub-matrices of the matrix quadratic forms in the skew elliptical variables and the moment generating functions and Bartlett's decomposition of the matrix quadratic forms in the skew normal variables. Two versions of the noncentral Cochran's Theorem for the matrix variate skew normal distributions are obtained, providing sufficient and necessary conditions for the quadratic forms in the skew normal variables to have the matrix variate noncentral generalized Dirichlet distributions. Applications include the properties of the least squares estimation in multivariate linear model and the robustness property of the Wilk's likelihood ratio statistic in the family of the matrix variate skew elliptical distributions.     

Likelihood ratio tests of correlated multivariate samples

We develop methods to compare multiple multivariate normally distributed samples which may be correlated. The methods are new in the context that no assumption is made about the correlations among the samples. Three types of null hypotheses are considered: equality of mean vectors, homogeneity of covariance matrices, and equality of both mean vectors and covariance matrices. We demonstrate that the likelihood ratio test statistics have finite-sample distributions that are functions of two independent Wishart variables and dependent on the covariance matrix of the combined multiple populations. Asymptotic calculations show that the likelihood ratio test statistics converge in distribution to central Chi-squared distributions under the null hypotheses regardless of how the populations are correlated. Following these theoretical findings, we propose a resampling procedure for the implementation of the likelihood ratio tests in which no restrictive assumption is imposed on the structures of the covariance matrices. The empirical size and power of the test procedure are investigated for various sample sizes via simulations. Two examples are provided for illustration. The results show good performance of the methods in terms of test validity and power.     

Likelihood robust optimization for data-driven problems

We consider optimal decision-making problems in an uncertain environment. In particular, we consider the case in which the distribution of the input is unknown, yet there is some historical data drawn from the distribution. In this paper, we propose a new type of distributionally robust optimization model called the likelihood robust optimization (LRO) model for this class of problems. In contrast to previous work on distributionally robust optimization that focuses on certain parameters (e.g., mean, variance, etc.) of the input distribution, we exploit the historical data and define the accessible distribution set to contain only those distributions that make the observed data achieve a certain level of likelihood. Then we formulate the targeting problem as one of optimizing the expected value of the objective function under the worst-case distribution in that set. Our model avoids the over-conservativeness of some prior robust approaches by ruling out unrealistic distributions while maintaining robustness of the solution for any statistically likely outcomes. We present statistical analyses of our model using Bayesian statistics and empirical likelihood theory. Specifically, we prove the asymptotic behavior of our distribution set and establish the relationship between our model and other distributionally robust models. To test the performance of our model, we apply it to the newsvendor problem and the portfolio selection problem. The test results show that the solutions of our model indeed have desirable performance.     

Asymptotic Corrections for Multivariate Posterior Moments with Factored Likelihood Functions

Abstract

Asymptotic corrections are used to compute the means and the variance-covariance matrix of multivariate posterior distributions that are formed from a normal prior distribution and a likelihood function that factors into separate functions for each variable in the posterior distribution. The approximations are illustrated using data from the National Assessment of Educational Progress (NAEP). These corrections produce much more accurate approximations than those produced by two different normal approximations. In a second potential application, the computational methods are applied to logistic regression models for severity adjustment of hospital-specific mortality rates.     

非线性回归模型中的约束拟似然

在非线性回归模型中,拟得分函数是一类线性无偏估计函数中的最优者(GodambeandHeyde(1987),朱仲义(1996)),而由拟得分函数得到的拟似然估计在由线性无偏估计函数得到的估计类中具有渐近最优性(林路(1999)).本文则研究非线性回归模型中的有偏估计函数理论,构造了参数的约束拟似然估计,得到了约束拟似然的局部最优性,局部改进了拟似然估计,从而扩充了线性模型中的有偏估计理论.     

非线性回归模型的经验似然诊断

经验似然方法已经被广泛用于线性模型和广义线性模型.本文基于经验似然方法对非线性回归模型进行统计诊断.首先得到模型参数的极大经验似然估计;其次基于经验似然研究了三种不同的影响曲率度量;最后通过一个实际例子,说明了诊断方法的有效性.