论文检索

小区域估计的拟经验贝叶斯方法AI 解读

This article develops an empirical Bayes-type approach for small area estimation based only on the specification of a set of conditionally independent hierarchical mean and variance functions describing the first two moments of the process generating the data. The objective of the analysis is to draw inference about the intermediate or the random means. We combine the quasi-likelihood functions to construct the quasi-posterior density of the random means conditional on the…

无信息先验与干扰参数AI 解读

We study the conflict between priors that are noninformative for a parameter of interest versus priors that are noninformative for the whole parameter. Our investigation leads us to maximize a functional that has two terms: an asymptotic approximation to a standardized expected Kullback-Leibler distance between the marginal prior and marginal posterior for a parameter of interest, and a penalty term measuring the distance of the prior from the Jeffreys prior. A positive…

Poly-Weibull 分布的贝叶斯分析AI 解读

In this article Bayesian analysis for a Poly-Weibull distribution using informative priors is discussed. This distribution typically arises when the data is the minimum of several Weibull failure times from competing risks. To perform the Bayesian computations, simulation using the Gibbs sampler is suggested. This can be used to find posterior moments, the marginal posterior probability density function, and the predictive risk or reliability.

小区域估计的拟经验贝叶斯方法AI 解读

This article develops an empirical Bayes-type approach for small area estimation based only on the specification of a set of conditionally independent hierarchical mean and variance functions describing the first two moments of the process generating the data. The objective of the analysis is to draw inference about the intermediate or the random means. We combine the quasi-likelihood functions to construct the quasi-posterior density of the random means conditional on the…

信息冲突何时产生渐近正态后验？AI 解读

When data conflicts with quantified prior beliefs, seemingly small changes in the functional form of the prior and/or likelihood can have a profound effect on posterior inferences. This article provides results on asymptotic forms of the posterior when two information sources conflict. In particular, let x be from the likelihood p 2(x − θ) with an unknown location parameter θ, with p 1(θ) the prior on θ. Sufficient conditions on p 1 and p 2 are provided to ensure that as x →…

指数族贝叶斯推断中的后验累积量关系AI 解读

For Bayesian inference in one-parameter contexts where either the likelihood or the prior has an exponential family form, relationships are derived for posterior moments and cumulants of (functions of) both the canonical and the expectation parameters. The identities exhibited generalize the simple relationships well known in the conjugate analysis case. Applications of these results are indicated in the areas of Bayesian robustness and approximation. In particular, results…

近似贝叶斯推断的 Bartlett 调整AI 解读

In wide generality, the posterior distributions of the likehood ratio statistic and the posterior ratio statistic are chi-squared to error of order O(n−1), where n is sample size. The error in the chi-squared approximation can be reduced to order O(n−2) by Bartlett correction. In this paper, explicit formulae are derived for the Bartlett adjustment factors of both statistics, and the derivations are based on the Tierney. Kass & Kadane (1989) asymptotic approximation for…

GARCH 模型的非平稳性：贝叶斯分析AI 解读

First, the non-stationarity properties of the conditional variances in the GARCH(1, 1) model are analysed using the concept of infinite persistence of shocks. Given a time sequence of probabilities for increasing/decreasing conditional variances, a theoretical formula for quasi-strict non-stationarity is defined. The resulting conditions for the GARCH(1,1) model are shown to differ from the weak stationarity conditions mainly used in the literature. Bayesian statistical…

燃油经济性模型中制造商效应的贝叶斯估计AI 解读

The analysis of fuel economy data results in estimates of the technology utilization by manufacturer and vehicle line. The analysis employs a hierarchical Bayesian regression model with random components representing vehicle lines and manufacturers. The model includes predictor variables which describe vehicle features, such as type of transmission, and vehicle line specific measurements, such as compression ratio. Non-informative priors with novel modifications are used and…

独立 Student-t 误差线性模型的贝叶斯分析AI 解读

This article takes up methods for Bayesian inference in a linear model in which the disturbances are independent and have identical Student-t distributions. It exploits the equivalence of the Student-t distribution and an appropriate scale mixture of normals, and uses a Gibbs sampler to perform the computations. The new method is applied to some well-known macroeconomic time series. It is found that posterior odds ratios favour the independent Student-t linear model over the…

摘要暂缺

近似非高斯贝叶斯估计与众数一致性AI 解读

SUMMARY A new recursive estimation procedure is proposed for the location of a dynamic linear model with non-normal errors. The procedure is a modification of a modal approximation algorithm, which is shown to be prone to instabilities. The modification is motivated by a notion of posterior modal consistency.

时间序列的稳健似然计算AI 解读

SUMMARY We propose a computationally efficient method for calculating the likelihoods of a time series under many submodels, each of which assumes a patch of outliers or level shifts. We assume a state space representation of the time series model with a Bayesian-type treatment of anomalies. The calculations form the basis for an efficient and robust estimation procedure. The method is also applicable to linear regression with correlated errors and is illustrated with two…

空间统计与贝叶斯计算AI 解读

SUMMARY Markov chain Monte Carlo (MCMC) algorithms, such as the Gibbs sampler, have provided a Bayesian inference machine in image analysis and in other areas of spatial statistics for several years, founded on the pioneering ideas of Ulf Grenander. More recently, the observation that hyperparameters can be included as part of the updating schedule and the fact that almost any multivariate distribution is equivalently a Markov random field has opened the way to the use of…

SUMMARY The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the context of some canonical examples. Other Markov chain Monte Carlo simulation methods are also briefly described, and comments are made on the advantages of sample-based approaches for Bayesian inference summaries.

似然比统计量带符号根的简易修正AI 解读

SUMMARY In the context of inference about a scalar parameter in the presence of nuisance parameters, some simple modifications for the signed root of the log-likelihood ratio statistic R are developed that reduce the order of error in the standard normal approximation to the distribution of R from O(n -1/2) to O(n -1). Barndorff-Nielsen has introduced a variable U such that the error in the standard normal approximation to the distribution of R + R -1 log(U/R) is of order…

贝叶斯方法的经验有效性AI 解读

SUMMARY The ideas in Kolmogorov's programme for algorithmic substantiation of applications of probability make it possible to define a measure of disagreement between the probability distribution representing the attitude of a coherent individual towards a random experiment and the outcome of the experiment. When there is agreement we say that the probability distribution is empirically valid. We prove quantitatively that formulae of Bayesian statistics transform empirically…

可分解图模型统计分析中的超 Markov 律AI 解读

This paper introduces and investigates the notion of a hyper Markov law, which is a probability distribution over the set of probability measures on a multivariate space that (i) is concentrated on the set of Markov probabilities over some decomposable graph, and (ii) satisfies certain conditional independence restrictions related to that graph. A stronger version of this hyper Markov property is also studied. Our analysis starts by reconsidering the properties of Markov…

信任预测与半贝叶斯推断AI 解读

We investigate the problem of fiducial prediction for unobserved quantities within the framework of the functional model described previously by Dawid and Stone. It is supposed that these are related to a completely unknown parameter by means of a regular functional model, and that the observations are either given as known functions of the predictands, or are themselves related to them by means of a functional model. We develop algebraic conditions which allow the…

漏计估计中未确定登记状态插补的层级逻辑回归模型AI 解读

In the process of collecting Post-Enumeration Survey (PES) data to evaluate census coverage, it is inevitable that there will be some individuals whose enumeration status (outcome in the census-PES match) remains unresolved even after extensive field follow-up operations. Earlier work developed a logistic regression framework for imputing the probability that unresolved individuals were enumerated in the census, so that the probability of having been enumerated is allowed to…

后分层：建模者的视角AI 解读

Post-stratification is a common technique in survey analysis for incorporating population distributions of variables into survey estimates. The basic technique divides the sample into post-strata, and computes a post-stratification weight w ih = rP h /r h for each sample case in post-stratum h, where r h is the number of survey respondents in post-stratum h, P h is the population proportion from a census, and r is the respondent sample size. Survey estimates, such as…

摘要暂缺

多事件时间数据的半参数贝叶斯分析AI 解读

Multiple event time data (e.g., carcinogenic growths in different times and locations, multiple attacks of cardiac arrest) arise in various medical studies. A Bayesian analysis of such data based on proportional intensity model of multiple event time data is presented in this paper. The Bayesian structure is somewhat analogous to that used by Kalbfleisch in a proportional hazard model. An unobserved random frailty component is used in the proportional intensity model to take…

自回归时间序列均值与方差变化的贝叶斯推断及预测AI 解读

This article is concerned with statistical inference and prediction of mean and variance changes in an autoregressive time series. We first extend the analysis of random mean-shift models to random variance-shift models. We then consider a method for predicting when a shift is about to occur. This involves appending to the autoregressive model a probit model for the probability that a shift occurs given a chosen set of explanatory variables. The basic computational tool we…