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通过预测相对熵遗憾构造非主观先验AI 解读

We explore the construction of nonsubjective prior distributions in Bayesian statistics via a posterior predictive relative entropy regret criterion. We carry out a minimax analysis based on a derived asymptotic predictive loss function and show that this approach to prior construction has a number of attractive features. The approach here differs from previous work that uses either prior or posterior relative entropy regret in that we consider predictive performance in…

具有近似频率学有效性的贝叶斯预测AI 解读

We characterize priors which asymptotically match the posterior coverage probability of a Bayesian prediction region with the corresponding frequentist coverage probability. This is done considering both posterior quantiles and highest predictive density regions with reference to a future observation. The resulting priors are shown to be invariant under reparameterization. The role of Jeffreys’ prior in this regard is also investigated. It is further shown that, for any…

贝叶斯—置信域的构造AI 解读

Summary We obtain approximate Bayes–confidence intervals for a scalar parameter based on directed likelihood. The posterior probabilities of these intervals agree with their unconditional coverage probabilities to fourth order, and with their conditional coverage probabilities to third order. These intervals are constructed for arbitrary smooth prior distributions. A key feature of the construction is that log-likelihood derivatives beyond second order are not required,…

近似条件推断的贝叶斯方法AI 解读

Sweeting (1995) studies regular Bayesian and frequentist approximations within a unified framework in the case of a single parameter, and shows that higher-order approximations to sampling distributions arise from their Bayesian counterparts via an unsmoothing argument. In the present paper we extend this programme to include formulae in approximate conditional inference. In particular it is shown how Bayesian arguments may be used to derive some formulae developed by…

贝叶斯与似然近似的统一统计框架AI 解读

Regular Bayesian and frequentist approximations in statistics are studied within a unified framework. In particular it is shown how some higher-order likelihood-based approximations arise from their Bayesian counterparts via an unsmoothing argument. This approach serves to shed new light on these formulae and to clarify relationships between Bayesian and frequentist inferences. For example, Bayesian interpretations of higher-order approximations can give insights into the…

基于参数的渐近方法AI 解读

Fisher's theory of maximum likelihood estimation routinely provides approximate confidence intervals for a parameter of interest θ, the standard intervals θ ^ ± zασ ^ where θ ^ is the maximum likelihood estimator, ô is an estimate of standard error based on differentiation of the log likelihood function, and zα is a normal percentile point. Recent work has produced systems of better approximate confidence intervals, which look more like exact intervals when exact intervals…

随机过程的渐近辅助性与条件推断AI 解读

Simple conditions on the observed information ensure asymptotic normality of the conditional distributions of the randomly normed score statistic and maximum likelihood estimator given a suitable asymptotically ancillary statistic. In particular, asymptotic normality holds conditional on any asymptotically ancillary statistic asymptotically equivalent to observed information. The results apply to inference from a general stochastic process and are of particular relevance in…

基于参数的渐近方法AI 解读

Fisher's theory of maximum likelihood estimation routinely provides approximate confidence intervals for a parameter of interest θ, the standard intervals θ ^ ± zασ ^ where θ ^ is the maximum likelihood estimator, ô is an estimate of standard error based on differentiation of the log likelihood function, and zα is a normal percentile point. Recent work has produced systems of better approximate confidence intervals, which look more like exact intervals when exact intervals…

删失生存数据的近似贝叶斯分析AI 解读

The Bayesian analysis of data arising from a location-scale regression model, and which may include a number of right-censored observations, is considered. The posterior distributions of the suitably normalized precision parameter and regression coefficient vector are shown to be well approximated by X2 and multivariate t distributions respectively. The ideas are illustrated using some clinical survival data.

删失生存数据的近似贝叶斯分析AI 解读

The Bayesian analysis of data arising from a location-scale regression model, and which may include a number of right-censored observations, is considered. The posterior distributions of the suitably normalized precision parameter and regression coefficient vector are shown to be well approximated by X2 and multivariate t distributions respectively. The ideas are illustrated using some clinical survival data.

位置—尺度回归模型的近似推断AI 解读

It is shown that χ2 and multivariate t distributions provide good approximations to posterior distributions arising in location-scale regression models. In the case of a linear regression model, Bayesian inferences based on an improper prior have conditional frequency, as well as fiducial/structural interpretation; so the results are also applicable in a non-Bayesian context. Numerical support for the approximations is presented for the case of a Weibull regression model and…

位置尺度回归模型的近似推断AI 解读

Abstract It is shown that χ2 and multivariate t distributions provide good approximations to posterior distributions arising in location-scale regression models. In the case of a linear regression model, Bayesian inferences based on an improper prior have conditional frequency, as well as fiducial/structural interpretation; so the results are also applicable in a non-Bayesian context. Numerical support for the approximations is presented for the case of a Weibull regression…

Box–Cox 变换线性模型的先验分布选择AI 解读

The noninformative prior distribution for the parameters of the linear model transformed following Box & Cox (1964) has the non-Bayesian property of depending to some extent on the data. An alternative choice of prior which is not outcome-dependent was suggested by Pericchi (1981), but it is argued here that this prior has some undesirable features. An alternative family of non-outcome-dependent priors is suggested, leading to a noninformative prior which is closer in spirit…

Box–Cox 变换线性模型的先验分布选择AI 解读

The noninformative prior distribution for the parameters of the linear model transformed following Box & Cox (1964) has the non-Bayesian property of depending to some extent on the data. An alternative choice of prior which is not outcome-dependent was suggested by Pericchi (1981), but it is argued here that this prior has some undesirable features. An alternative family of non-outcome-dependent priors is suggested, leading to a noninformative prior which is closer in spirit…