日期精度：到日 2008 篇 · 到月 2034 篇 · 到年 253 篇 · 未提供 1 篇
非参数经验贝叶斯生长曲线分析
Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.
弓头鲸确定性种群动力学模型的统计推断
We consider the problem of inference about a quantity of interest given different sources of information linked by a deterministic population dynamics model. Our approach consists of translating all the available information into a joint premodel distribution on all the model inputs and outputs and then restricting this to the submanifold defined by the model to obtain the joint postmodel distribution. Marginalizing this yields inference, conditional on the model, about…
非参数经验贝叶斯生长曲线分析
Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis. In this article we demonstrate that empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.
信息性一般删失下分类数据的贝叶斯方法
This paper develops a Bayesian approach to the problem of incomplete categorical data informatively censored where the reported sets are not restricted to follow any specific pattern. It generalises that introduced by Paulino & Pereira (1992) in not requiring a censoring pattern by partitions of the set of sampling categories. Some extensions are also discussed.
时间序列模型的模拟平滑器
Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding the degeneracies inherent in state samplers. The technique is important in Gibbs sampling with non-Gaussian time series models, and for performing Bayesian analysis of Gaussian…
将两时期交叉设计的贝叶斯分析扩展至缺失数据
A procedure is developed for performing a Bayesian analysis of a simple two-period crossover design when some observations are missing in one, or both, treatment periods. The approach requires the numerical evaluation of a single integral to be performed for each posterior marginal distribution of interest. The relative efficiency of this approach to one in which patients with missing data are excluded is investigated.
使贝叶斯推断具有频率学有效性的先验
We derive the differential equation that a prior must satisfy if the posterior probability of a one-sided credibility interval for a parametric function and its frequentist probability agree up to 0(n−1). This equation turns out to be identical with Stein's equation for a slightly different problem, for which also our method provides a rigorous justification. Our method is different in details from Stein's but similar in spirit to Dawid (1991) and Bickel & Ghosh (1990). Some…
贝叶斯分析中有界影响的一个注记
Let χ be a single observation from a distribution having unknown location parameter θ. Dawid (1973) provided sufficient conditions for the posterior distribution of θ to approach the prior distribution as χ tends to infinity, so that an outlier has bounded and vanishing influence on the posterior distribution. We present a result closely related to Dawid's theorem. This enables us to consider priors and likelihoods for the location problem which have bounded but nonvanishing…
近似条件推断的贝叶斯方法
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…
单因素随机效应模型的最优贝叶斯实验设计
Since variance components are nonlinear parameters in a linear model, classical optimal designs for estimating variance components in the one-way random effects model depend on the values of the parameters. Optimal Bayesian designs are derived for the more realistic situation of unknown parameters, using the Fisher information of the variance components as a basis for comparing the designs. Balanced designs, in which the same number of measurements are taken on each unit,…
统计中的贝叶斯与似然近似统一框架
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…
融入专家知识的泊松点过程推断与预测
We present a Bayesian approach for inference and predictions from nonhomogeneous Poisson point processes. The novel feature of our approach is the use of “expert knowledge” or “engineering information” on the mean value function of the process. We describe two scenarios from the field of reliability in which engineering information on the mean value function is available. The first scenario pertains to the prediction of software failures during the debugging phase. Here…
线性模型中的一致变量选择
A method of estimating linear model dimension and variable selection is proposed. This new criterion, which generalizes the Cp criterion, the Akaike information criterion (AIC), the Bayes information criterion, and the phiv criterion and is consistent under certain conditions, is based on a new class of penalty functions and a procedure of sorting covariates based on t-statistics. In the course of introducing this method, we discuss the important role of the penalty function…
用于假设检验的多重贝叶斯因子
Partial and multiple Bayes factors are introduced to obtain pairwise comparisons of hypotheses in a statistical experiment with a partition on the parameter space. Robust Bayesian analyses are performed by introducing suitable classes of priors and by calculating lower and upper bounds of Bayes factors and posterior probabilities. Classes of intuitively meaningful priors are introduced, including unimodal densities without the constraint of symmetry for the case of precise…
风险率回归
Linear splines and their tensor products are used to estimate the conditional log-hazard function based on possibly censored, positive response data and one or more covariates. An automatic procedure involving the maximum likelihood method, stepwise addition, stepwise deletion, and the Bayes Information Criterion is used to select the final model. The possible models contain proportional hazards models as a subclass, which makes it possible to diagnose departures from…
分拆问卷调查设计
This article develops a survey design where the questionnaire is split into components and individuals are administered the varying subsets of the components. A multiple imputation method for analyzing data from this design is developed, in which the imputations are created by random draws from the posterior predictive distribution of the missing parts, given the observed parts by using Gibbs sampling under a general location scale model. Results from two simulation studies…
保存肿瘤标本取样方案的贝叶斯分析
Intratumor DNA heterogeneity—as assessed by flow-cytometric cellular DNA analysis—is predictive of prognosis in many types of cancer. But some truly heterogeneous tumors apparently have a substantial chance of being misclassified as normal. One way to enhance the assay's sensitivity is to test multiple samples from each tumor specimen. This article describes an analysis of data from a study of endometrial cancer, with a view to establishing the number of samples needed to…
用于假设检验的多重贝叶斯因子
Partial and multiple Bayes factors are introduced to obtain pairwise comparisons of hypotheses in a statistical experiment with a partition on the parameter space. Robust Bayesian analyses are performed by introducing suitable classes of priors and by calculating lower and upper bounds of Bayes factors and posterior probabilities. Classes of intuitively meaningful priors are introduced, including unimodal densities without the constraint of symmetry for the case of precise…
保存肿瘤标本取样方案的贝叶斯分析
Intratumor DNA heterogeneity—as assessed by flow-cytometric cellular DNA analysis—is predictive of prognosis in many types of cancer. But some truly heterogeneous tumors apparently have a substantial chance of being misclassified as normal. One way to enhance the assay's sensitivity is to test multiple samples from each tumor specimen. This article describes an analysis of data from a study of endometrial cancer, with a view to establishing the number of samples needed to…
贝叶斯分析中有界影响的一则说明
Let χ be a single observation from a distribution having unknown location parameter θ. Dawid (1973) provided sufficient conditions for the posterior distribution of θ to approach the prior distribution as χ tends to infinity, so that an outlier has bounded and vanishing influence on the posterior distribution. We present a result closely related to Dawid's theorem. This enables us to consider priors and likelihoods for the location problem which have bounded but nonvanishing…
单向随机效应模型的最优贝叶斯实验设计
Since variance components are nonlinear parameters in a linear model, classical optimal designs for estimating variance components in the one-way random effects model depend on the values of the parameters. Optimal Bayesian designs are derived for the more realistic situation of unknown parameters, using the Fisher information of the variance components as a basis for comparing the designs. Balanced designs, in which the same number of measurements are taken on each unit,…
近似条件推断的贝叶斯方法
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
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…