多元线性回归模型的贝叶斯统计推断

贝叶斯统计学是统计学的两个重要分支之一,可广泛应用于通信、人工智能、地震、地质勘探、气象预报和经济管理等领域.本文讨论贝叶斯方法在多元回归分析中的应用.     

应积极开展对贝叶斯学派的介绍和研究

统计学中由于观点不同,形成了各种学派,其中影响较大的是频率学派、贝叶斯学派和信念(Fiducial)学派。在目前我国出版的教材中,绝大部分都是频率学派(或称经典学派)的观点、理论和方法,其余两派的观点、理论和方法却很少见。但是在实际应用部门,如航天工程、电子工业等有关单位,却早已应用贝叶斯方法和信念方法来     

妙用贝叶斯统计三则

提到贝叶斯统计,人们就会立刻想到“逆概率”,然而在20世纪初,统计学家们都非常谨慎地避免使用“逆概率”。在一次皇家统计学会上,学者们对费歇尔的一篇早期论文进行讨论时,有人质疑他运用了“逆概率”,他坚定地为自己辩护,否认这项可怕的指控。在费歇尔的研究过程中,曾有好几次他差点儿就建立起一门新的统计学分支学科,但每次他都半途而废。“逆概率”的思想起源于18世纪后期,由英国牧师托马斯·贝叶斯发明,但就连贝叶斯本人也把相关的论文和分析结果搁置起来,直到其去世之后,这些论文才得以发表。可以说贝叶斯统计是在与经典统计的争论中…     

分层线性模型中的经验贝叶斯与完全贝叶斯方法及其比较

文章对基于最大似然估计(ML)和基于约束最大似然估计(REML)的经验贝叶斯方法(EB)进行了比较;指出了经验贝叶斯和完全贝叶斯方法各自存在的问题,并对两种方法进行了比较:给出了关于应用中如何选择推断方法的建议.     

面板数据的贝叶斯Elastic Net分位数回归方法及其应用研究

本文首次将Elastic Net这种用于高度相关变量的惩罚方法用于面板数据的贝叶斯分位数回归,并基于非对称Laplace先验分布推导所有参数的后验分布,进而构建Gibbs抽样。为了验证模型的有效性,本文将面板数据的贝叶斯Elastic Net分位数回归方法(BQR. EN)与面板数据的贝叶斯分位数回归方法(BQR)、面板数据的贝叶斯Lasso分位数回归方法(BLQR)、面板数据的贝叶斯自适应Lasso分位数回归方法(BALQR)进行了多种情形下的全方位比较,结果表明BQR. EN方法适用于具有高度相关性、数据维度很高和尖峰厚尾分布特征的数据。进一步地,本文就BQR. EN方法在不同扰动项假设、不同样本量的情形展开模拟比较,验证了新方法的稳健性和小样本特性。最后,本文选取互联网金融类上市公司经济增加值(EVA)作为实证研究对象,检验新方法在实际问题中的参数估计与变量选择能力,实证结果符合预期。     

贝叶斯统计推断及其主要进展

贝叶斯统计推断作为现代统计分析方法的重要内容,对于统计学理论的发展具有里程碑的作用。深入总结其研究的主要进展,具有重要的现实意义。在查阅国内外重要学术研究资料的基础上,从贝叶斯统计推断的思想、与古典统计的研究思路比较和贝叶斯统计推断研究的主要进展三个方面作了综述与介绍,力图达到认识贝叶斯统计推断及其研究现状的目的。     

“杰弗里原则”及其对计量经济学的影响简介

对"杰弗里原则"的提出及其影响做了介绍。第一节是杰弗里生平,介绍他在物理学及统计学方面取得的成就;第二节介绍以他姓氏命名的"杰弗里原则",这是对选择先验概率的"贝叶斯法则"的重大修正;第三节介绍基于"杰弗里先验"的"信息守恒原则",此原则对当代贝叶斯计量经济学有重大影响。     

中国海关统计50年

海关统计 (ｃｕｓｔｏｍｓｓｔａｔｉｓｔｉｃｓ)是由中国海关收集和编制的中国进出口货物贸易统计 ,是中国国民经济统计的组成部分 ,也是研究中国对外经济贸易发展及国际经济贸易关系的重要资料。 194 9年中华人民共和国建立前夕 ,中国海关已有近百年进出口货物的完整统计资料。 195 0年 ,中央人民政府政务院总理周恩来为《海关统计》刊物题写了刊名 ,“海关统计”自此成为中华人民共和国对外商品贸易统计的代名词。海关统计是国家赋予海关的一项基本职能。其任务是 :对进出中华人民共和国关境的货物进行统计调查和统计分析 ,科学、准确地…     

基于软件Eviews和WinBUGS计量模型的比较

在计量经济学设计模型、收集资料、估计模型、检验模型、应用模型(结构分析、经济预测、政策评价)过程中,Eviews软件是必不可少的工具,而WinBUGS软件是贝叶斯计量经济学计算的常用软件.文章以时间序列AR(1)模型为例,运用Gibbs抽样的MCMC方法,介绍了贝叶斯统计方法在计量经济学模型中的应用,分析了在实证中贝叶斯估计与经典计量估计的区别和联系,并指出贝叶斯方法在计量经济学及其他学科广阔的应用前景.     

贝叶斯决策理论概述

贝叶斯统计学包括贝叶斯决策理论、贝叶斯估计及贝叶斯回归分析等内容。本文仅就贝叶斯决策理论的基本概念和方法作一简介。一、后验概率分布统计决策理论接是否抽取样本可分为先验和后验两种概率决策。先验概率决策所依据的先验概率分布是根据以往积累的资料和经验估计所确定的,并认为现在总体仍维持着这样的     

MCMC方法的发展与现代贝叶斯的复兴——纪念贝叶斯定理发现250周年

信息科学的进步如何影响统计学理论与方法的发展,是21世纪大数据时代统计学面临的重要问题。回顾20世纪40年代MCMC方法的起源与发展,基于千岛湖植物考察与R软件直观性解释Metropolis–Hastings算法,讨论MCMC方法的发展对现代贝叶斯统计复兴具有至关重要的作用。基于云计算随机模拟,统计学与信息科学密切结合、贝叶斯与频率统计相互交融的统计分析新范式,可能成为大数据研究的新思路。     

Bayesian hierarchical models for adaptive basket trial designs

Basket trials evaluate a single drug targeting a single genetic variant in multiple cancer cohorts. Empirical findings suggest that treatment efficacy across baskets may be heterogeneous. Most modern basket trial designs use Bayesian methods. These methods require the prior specification of at least one parameter that permits information sharing across baskets. In this study, we provide recommendations for selecting a prior for scale parameters for adaptive basket trials by using Bayesian hierarchical modeling. Heterogeneity among baskets attracts much attention in basket trial research, and substantial heterogeneity challenges the basic assumption of exchangeability of Bayesian hierarchical approach. Thus, we also allowed each stratum-specific parameter to be exchangeable or nonexchangeable with similar strata by using data observed in an interim analysis. Through a simulation study, we evaluated the overall performance of our design based on statistical power and type I error rates. Our research contributes to the understanding of the properties of Bayesian basket trial designs.     

Bayesian inference for categorical data analysis

This article surveys Bayesian methods for categorical data analysis, with primary emphasis on contingency table analysis. Early innovations were proposed by Good (1953, 1956, 1965) for smoothing proportions in contingency tables and by Lindley (1964) for inference about odds ratios. These approaches primarily used conjugate beta and Dirichlet priors. Altham (1969, 1971) presented Bayesian analogs of small-sample frequentist tests for 2 x 2 tables using such priors. An alternative approach using normal priors for logits received considerable attention in the 1970s by Leonard and others (e.g., Leonard 1972). Adopted usually in a hierarchical form, the logit-normal approach allows greater flexibility and scope for generalization. The 1970s also saw considerable interest in loglinear modeling. The advent of modern computational methods since the mid-1980s has led to a growing literature on fully Bayesian analyses with models for categorical data, with main emphasis on generalized linear models such as logistic regression for binary and multi-category response variables.     

Bayesian robustness of the compound Poisson distribution under bidimensional prior: an application to the collective risk model

The distribution of the aggregate claims in one year plays an important role in Actuarial Statistics for computing, for example, insurance premiums when both the number and size of the claims must be implemented into the model. When the number of claims follows a Poisson distribution the aggregated distribution is called the compound Poisson distribution. In this article we assume that the claim size follows an exponential distribution and later we make an extensive study of this model by assuming a bidimensional prior distribution for the parameters of the Poisson and exponential distribution with marginal gamma. This study carries us to obtain expressions for net premiums, marginal and posterior distributions in terms of some well-known special functions used in statistics. Later, a Bayesian robustness study of this model is made. Bayesian robustness on bidimensional models was deeply treated in the 1990s, producing numerous results, but few applications dealing with this problem can be found in the literature.     

Re-examining informative prior elicitation through the lens of Markov chain Monte Carlo methods

Summary.  In recent years, advances in Markov chain Monte Carlo techniques have had a major influence on the practice of Bayesian statistics. An interesting but hitherto largely underexplored corollary of this fact is that Markov chain Monte Carlo techniques make it practical to consider broader classes of informative priors than have been used previously. Conjugate priors, long the workhorse of classic methods for eliciting informative priors, have their roots in a time when modern computational methods were unavailable. In the current environment more attractive alternatives are practicable. A reappraisal of these classic approaches is undertaken, and principles for generating modern elicitation methods are described. A new prior elicitation methodology in accord with these principles is then presented.     

Some topics of current interest in clinical trials

After a short review of the development of the modern approach to the design of clinical trials, attention is focused on the wide variety of trials, their ethical constraints, the criteria for stopping trials, and the possible use of Bayesian methods. Brief discussions are then presented of two specific topics: the analysis of categorical data, and the replication of trials with the consequent need for overviews.     

谁开创了贝叶斯学派？——对拉普拉斯1774年一篇文章的回顾

1764年,贝叶斯的遗作《机遇理论中一个问题的解》，是当今统计学界贝叶斯学派的开山之作，然而直到20世纪，贝叶斯的文章却并未对后世贝叶斯学派的发展产生重大影响。事实上，真正促使贝叶斯的思想在科学界得到广泛传播的关键人物是大科学家拉普拉斯。1774年，拉普拉斯发表的关于贝叶斯理论的里程碑式的文章，使现代读者充分认识到拉普拉斯对贝叶斯学派发展所起的巨大作用，并从拉普拉斯的思想中获得了有益的新知。     

A permutation-based Bayesian approach for inverse covariance estimation

Abstract

Covariance estimation and selection for multivariate datasets in a high-dimensional regime is a fundamental problem in modern statistics. Gaussian graphical models are a popular class of models used for this purpose. Current Bayesian methods for inverse covariance matrix estimation under Gaussian graphical models require the underlying graph and hence the ordering of variables to be known. However, in practice, such information on the true underlying model is often unavailable. We therefore propose a novel permutation-based Bayesian approach to tackle the unknown variable ordering issue. In particular, we utilize multiple maximum a posteriori estimates under the DAG-Wishart prior for each permutation, and subsequently construct the final estimate of the inverse covariance matrix. The proposed estimator has smaller variability and yields order-invariant property. We establish posterior convergence rates under mild assumptions and illustrate that our method outperforms existing approaches in estimating the inverse covariance matrices via simulation studies.     

Bayesian Analysis of DNA Profiling Data in Forensic Identification Applications

The utilization of DNA evidence in cases of forensic identification has become widespread over the last few years. The strength of this evidence against an individual standing trial is typically presented in court in the form of a likelihood ratio (LR) or its reciprocal (the profile match probability). The value of this LR will vary according to the nature of the genetic relationship between the accused and other possible perpetrators of the crime in the population. This paper develops ideas and methods for analysing data and evaluating LRs when the evidence is based on short tandem repeat profiles, with special emphasis placed on a Bayesian approach. These are then applied in the context of a particular quadruplex profiling system used for routine case-work by the UK Forensic Science Service.