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．     

限制参数空间上的Fiducial推断

给出了在限制参数空间上,利用Fiducial方法求参数的区间估计的一般方法,并且讨论了一些常见的典型问题,结果表明所得的区间估计是合理的．另外,本文还证明了在限制参数空间上,刻度族和位置族中参数的条件Fiducial分布与无信息先验的Bayes 后验分布一致,推广了Lindely的结论．     

枢轴分布族中的Fiducial推断

研究枢轴分布族下的Fiducial推断, 提出了求参数的Fiducial分布的一般方法, 在这个方法中Fiducial模型起了重要作用. 方法包含了一些其他求Fiducial分布的方法, 特别地, Fisher提出的第1个Fiducial分布可由此方法导出. 对于单调似然比分布族这样的枢轴分布族, Fiducial分布具有一个Neyman-Pearson观点上的频率性质, 对于文中定义的一类正规参数函数, 它的Fiducial分布也具有上述频率性质. Fiducial推断的一些优点在Fiducial分布的4个应用中展示. 给出了许多例子, 其中的一些例子用现有的方法是得不到Fiducial分布的.     

不同先验分布下的后验分布确定土力学参数

岩土工程中各土层参数的取值是根据现场及室内试验数据,采用经典统计学方法进行确定的,但这往往忽略了先验信息的作用.与经典统计学方法不同的是,Bayes法能从考虑先验分布的角度结合样本分布去推导后验分布,为岩土参数的取值提供一种新的分析方法.岩土工程勘察可视为对总体地层的随机抽样,当抽样完成时,样本分布密度函数是确定的,故Bayes法中的后验分布取决于先验分布,因此推导出两套不同的先验分布:利用先验信息确定先验分布及共轭先验分布.通过对先验及后验分布中超参数的计算,当样本总体符合N(μ,σ²)正态分布时,对所要研究的未知参数μ和σ展开分析,综合对比不同先验分布下后验分布的区间长度,给出岩土参数Bayes推断中最佳后验分布所要选择的先验分布.结果表明:共轭情况下的后验分布总是比无信息情况下的后验区间短,概率密度函数分布更集中,取值更方便.在正态总体情形下,根据未知参数μ和σ的联合后验分布求极值方法,确定样本总体中最大概率均值μ_max和方差σ_max作为工程设计采用值,为岩土参数取值方法提供了一条新的路径,有较好的工程意义.     

有缺失数据的正态母体参数的后验分布及其抽样算法

在缺失数据机制是可忽略的、先验分布是逆矩阵Γ分布的假设下,利用矩阵的cholesky分解和变量替换方法,本文导出了有单调缺失数据结构的正态分布参数的后验分布形式.进-步用后验分布的组成特点,构造了单调缺失数据结构的正态分布的协方差矩阵和均值后验分布的抽样算法.     

截断型分布族参数经验Bayes检验的收敛速度

§ 1.　Introduction　　SupposethatrandomvariableXhaspdf(forLebesguemeasure)f(x｜θ) =u(x)m(θ)I(θ ,b) (x) ,(1 )whereθ>a≥ 0 ,θisthetruncationparameterofourinterestandb≤+∞ ,u(x)ispositiveLebesgueintegrablefunctionon (θ,b) ,m(θ) =[∫bθ(u(x)dx] - 1 .ThehypothesistobetestedisH0 ;θ≤θ0 H1 :θ >θ0 , (2 )whereθ0 isaknownconstant.LetlossfunctionisL(θ ,a0 ) =b0 max(θ -θ0 ,0 )foracceptingH0 andL(θ,a1 ) =b0 max(θ0 -θ,0 )foracceptingH1 ,whereb0 isapositiverealnumber,D ={a0 ,a1 }isth…     

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。     

正态分布的共轭分布及贝叶斯估计

本文介绍含有一个或两个未知参数的正态分布Ｎ（μ，）的共轭分布，以及对正态总体的未知参数进行估计的贝叶斯方法。     

先验参数分布族Г下估计的Г—容许性

本文讨论了分布族参数的先验参数分布族Г下的Г容许性问题，并对先验分布族参数受到限制时相应估计的Г容许性进行了研究。     

多源验前信息下先验分布的稳健融合方法

根据多种先验分布与似然函数尾部特性的比较,给出了多源验前信息下先验分布的稳健融合方法.讨论了由该方法得到的融合先验分布的后验稳健性问题.最后的数值例子表明,由该方法得到的融合先验分布具有较好的稳健性,进一步验证了该方法的有效性.     

位置刻度分布族中参数的两步置信区间

寻找一些分布中的参数的具有预先给定宽度和预先给定覆盖概率的置信区间是令人感兴趣的，对于位置刻度分布族中位置参数和刻度参数，这种类型的置信区间的存在性问题已在文献中被解决，本文利用两步抽样，具体地构造出这样的固定宽度置信区间，此外，对于Cauchy分布，第一阶段的最优抽样量和一些统计量的分位点也被计算出，所得到的结果具有应用价值。     

Asymptotic Expansions of Posterior Distributions in Nonregular Cases

We study the asymptotic behaviour of the posterior distributions for a one-parameter family of discontinuous densities. It is shown that a suitably centered and normalized posterior converges almost surely to an exponential limit in the total variation norm. Further, asymptotic expansions for the density, distribution function, moments and quantiles of the posterior are also obtained. It is to be noted that, in view of the results of Ghosh et al. (1994, Statistical Decision Theory and Related Topics V, 183-199, Springer, New York) and Ghosal et al. (1995, Ann. Statist., 23, 2145-2152), the nonregular cases considered here are essentially the only ones for which the posterior distributions converge. The results obtained here are also supported by a simulation experiment.     

Multiway Dependence in Exponential Family Conditional Distributions

Conditionally specified statistical models are frequently constructed from one-parameter exponential family conditional distributions. One way to formulate such a model is to specify the dependence structure among random variables through the use of a Markov random field (MRF). A common assumption on the Gibbsian form of the MRF model is that dependence is expressed only through pairs of random variables, which we refer to as the “pairwise-only dependence” assumption. Based on this assumption, J. Besag (1974, J. Roy. Statist. Soc. Ser. B36, 192–225) formulated exponential family “auto-models” and showed the form that one-parameter exponential family conditional densities must take in such models. We extend these results by relaxing the pairwise-only dependence assumption, and we give a necessary form that one-parameter exponential family conditional densities must take under more general conditions of multiway dependence. Data on the spatial distribution of the European corn borer larvae are fitted using a model with Bernoulli conditional distributions and several dependence structures, including pairwise-only, three-way, and four-way dependencies.     

Distributions Minimizing Fisher Information for Location in Kolmogorov Neighbourhoods

This paper is concerned with the problem of finding those distributionsminimizing Fisher information for location over Kolmogorov neighbourhoods ofdistribution functions G satisfying certain mild conditions. The case when Gis symmetric has been considered by quite a few authors. The general form ofthe solution is discussed. Furthermore we provide the solution of twoasymmetric distributions, namely, the extreme value distribution and thePearson's type IV distribution.     

Information Inequalities in a Family of Uniform Distributions

For a family of uniform distributions, it is shown that for any small < 0 the average mean squared error (MSE) of any estimator in the interval of values of length and centered at ₀ can not be smaller than that of the midrange up to the order o(n ^–2) as the size n of sample tends to infinity. The asymptotic lower bound for the average MSE is also shown to be sharp.     

多项分布与Dirichlet分布概率的相互表示

讨论了四种多项分布尾概率与四种Dirichlet分布尾概率的相互表示,并将结果应用于Majorization理论,得到了多项分布和Dirichlet分布对应的一些性质.同时,结果可应用于多项分布最大、最小参数的贝叶斯推断,在佛罗里达州沃尔顿县白人和黑人的职业状态调查结果中,求出最大、最小参数的后验分布函数以及95%贝叶斯区间估计,模拟结果表明提供的方法具有较好的表现.     

Properties of Prior and Posterior Distributions for Multivariate Categorical Response Data Models

In this article, we model multivariate categorical (binary and ordinal) response data using a very rich class of scale mixture of multivariate normal (SMMVN) link functions to accommodate heavy tailed distributions. We consider both noninformative as well as informative prior distributions for SMMVN-link models. The notation of informative prior elicitation is based on available similar historical studies. The main objectives of this article are (i) to derive theoretical properties of noninformative and informative priors as well as the resulting posteriors and (ii) to develop an efficient Markov chain Monte Carlo algorithm to sample from the resulting posterior distribution. A real data example from prostate cancer studies is used to illustrate the proposed methodologies.     

非正则平移分布位置参数极大似然估计的极限分布及渐近有效性

In this paper, we consider the location model Y=θ e,where θ is an unknown parameter, and e is the error belonging to the interval [a, b]. We assume that e has the following density function.     

焦点量与鞍点量的关系

对于一般情形,给出焦点量和鞍点量计算与约化的Maple算法,从而统一了焦点量和鞍点量的计算,并给出细焦点与细鞍点的变换,利用变换推导了焦点量和鞍点量的关系．