论文检索

微阵列实验的经验贝叶斯分析AI 解读

Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment in which oligonucleotide arrays were employed to assess the genetic effects of ionizing radiation on seven thousand human genes. A simple nonparametric empirical Bayes model is…

半参数非线性混合效应模型及其应用AI 解读

Nonlinear mixed effects models (NLMMs) and self-modeling nonlinear regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model variation within each subject and some fixed and/or random parameters to model variation between subjects. The parametric NLMM may be too restrictive, and the semiparametric SEMOR model ignores correlations within each subject. In this article we propose a class of semiparametric…

物种组成的统计解释AI 解读

The relative abundance of different species characterizes the structure of a biological community. We analyze an experiment addressing the relationship between omnivorous feeding linkages and community stability. Our goal is to determine whether communities with different predator compositions respond similarly to environmental disturbance. To evaluate these data, we develop a hierarchical statistical model that combines Aitchison's logistic normal distribution with a…

美国社区供水系统源水砷含量的刻画AI 解读

We develop a Bayesian hierarchical model linking arsenic concentrations in the source waters of U.S. community water systems to system characteristics such as source water type and location. This characterization provides a starting point for the assessment of current and required water treatment to meet proposed maximum contaminant level (MCL) standards. After a model validation study based on predictive densities, we use a national census of treatment systems and their…

利用二重条件自回归模型改进死亡率估计的双变量贝叶斯方法AI 解读

A bivariate Bayes method is proposed for estimating the mortality rates of a single disease for a given population, using additional information from a second disease. The information on the two diseases is assumed to be from the same population groups or areas. The joint frequencies of deaths for the two diseases for given populations are assumed to have a bivariate Poisson distribution with joint means proportional to the population sizes. The relationship between the…

检测非线性个体曲线急剧变化的贝叶斯模型AI 解读

We propose a model for longitudinal data with random effects that includes model-based smoothing of measurements over time. This research is motivated by experiments evaluating the hemodynamic effects of various agents in tumor-bearing rats. In one set of experiments, the rats breathed room air, followed by carbogen (a mixture of pure oxygen and carbon dioxide). The experimental responses are longitudinal measurements of oxygen pressure measured in tissue, tumor blood flow,…

利用MCMC对时间相关数据进行多元受体建模AI 解读

Multivariate receptor modeling aims to estimate pollution source profiles and the amounts of pollution based on a series of ambient concentrations of multiple chemical species over time. Air pollution data often show temporal dependence due to meteorology and/or background sources. Previous approaches to receptor modeling do not incorporate this dependence. We model dependence in the data using a time series approach so that we can incorporate extra sources of variability in…

通常似然与调整似然所构造多维置信集的期望体积AI 解读

Summary We consider a general multiparameter set-up, where both the interest and the nuisance parameters are possibly vector valued. We derive an explicit higher order asymptotic formula to compare the expected volumes of confidence sets given by likelihood ratio statistics arising from the usual profile likelihood and various adjustments thereof. Our general framework also allows us to include highest posterior density regions, with approximate frequentist validity, in the…

论贝叶斯一致性AI 解读

Summary We consider a sequence of posterior distributions based on a data-dependent prior (which we shall refer to as a pseudoposterior distribution) and establish simple conditions under which the sequence is Hellinger consistent. It is shown how investigations into these pseudo posteriors assist with the understanding of some true posterior distributions, including Pólya trees, the infinite dimensional exponential family and mixture models.

企业与时间变化的等式及不等式约束下的经济关系估计AI 解读

Abstract Applied econometricians often fail to impose economic regularity constraints in the exact form economic theory prescribes. We show how the Singular Value Decomposition (SVD) Theorem and Markov Chain Monte Carlo (MCMC) methods can be used to rigorously impose time‐ and firm‐varying equality and inequality constraints. To illustrate the technique we estimate a system of translog input demand functions subject to all the constraints implied by economic theory,…

多元正态观测的收缩预测分布AI 解读

We investigate shrinkage methods for constructing predictive distributions. We consider the multivariate Normal model with a known covariance matrix and show that there exists a shrinkage predictive distribution dominating the Bayesian predictive distribution based on the vague prior when the dimension is not less than three. Kullback–Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function.

覆盖概率偏差、客观贝叶斯与似然原则AI 解读

We review objective Bayes procedures based on both parametric and predictive coverage probability bias and explore the extent to which such procedures contravene the likelihood principle in the case of a scalar parameter. The discussion encompasses choice of objective priors, objective posterior probability statements and objective predictive probability statements. We conclude with some remarks concerning the future development and implementation of objective priors based…

合并可能相似的实验数据AI 解读

Given data from L experiments or observational studies initially believed to be similar, it is desired to estimate the mean corresponding to an experiment or observational study, Ej, of particular interest. It is often profitable to use the data from related studies to sharpen the estimate corresponding to experiment Ej . However, it is essential that all of the data that are combined be concordant with the data from Ej. We improve the methodology first proposed by Malec &…

神经网络中的贝叶斯推断AI 解读

Approximate marginal Bayesian computation and inference are developed for neural network models. The marginal considerations include determination of approximate Bayes factors for model choice about the number of nonlinear sigmoid terms, approximate predictive density computation for a future observable and determination of approximate Bayes estimates for the nonlinear regression function. Standard conjugate analysis applied to the linear parameters leads to an explicit…

利用Metropolis–Hastings算法推断部分观测非线性扩散模型AI 解读

In this paper, we introduce a new Markov chain Monte Carlo approach to Bayesian analysis of discretely observed diffusion processes. We treat the paths between any two data points as missing data. As such, we show that, because of full dependence between the missing paths and the volatility of the diffusion, the rate of convergence of basic algorithms can be arbitrarily slow if the amount of the augmentation is large. We offer a transformation of the diffusion which breaks…

正态混合密度极大似然与贝叶斯估计的熵及收敛速度AI 解读

We study the rates of convergence of the maximum likelihood estimator (MLE) and posterior distribution in density estimation problems, where the densities are location or location-scale mixtures of normal distributions with the scale parameter lying between two positive numbers. The true density is also assumed to lie in this class with the true mixing distribution either compactly supported or having sub-Gaussian tails. We obtain bounds for Hellinger bracketing entropies…

Bernstein 多项式密度估计的收敛速度AI 解读

Mixture models for density estimation provide a very useful set up for the Bayesian or the maximum likelihood approach.For a density on the unit interval, mixtures of beta densities form a flexible model. The class of Bernstein densities is a much smaller subclass of the beta mixtures defined by Bernstein polynomials, which can approximate any continuous density. A Bernstein polynomial prior is obtained by putting a prior distribution on the class of Bernstein densities. The…

计算机模型的贝叶斯校准AI 解读

Summary We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that by a suitable choice of some of the model's input parameters the code can be used to predict the behaviour of the system in a variety of specific applications. However, in any specific application the values of necessary parameters may be unknown. In this case, physical…

摘要暂缺

多期控制的贝叶斯启发式方法AI 解读

In multiperiod control problems, one needs to sequentially choose the value of a set of multiple input variables, xn, to keep the value of a single output variable, yn, close to a target, T. At any stage n, the selection of xn poses a difficult dilemma. On the one hand, xn has to be such that (yn(xn) - T)2 is small in expectation. On the other hand, xn has to be such that it maximizes the amount of information in yn(xn) about the model relating yn and xn, to help keep the…

计算 Bayes 因子的马尔可夫链蒙特卡洛方法AI 解读

The problem of calculating posterior probabilities for a collection of competing models and associated Bayes factors continues to be a formidable challenge for applied Bayesian statisticians. Current approaches that take advantage of modern Markov chain Monte Carlo computing methods include those that attempt to sample over some form of the joint space created by the model indicators and the parameters for each model, others that sample over the model space alone, and still…

随机块结构的估计与预测AI 解读

A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned into several unobserved (latent) classes and that the probability distribution of the relation between two vertices depends only on the classes to which they belong. A Bayesian estimator based on Gibbs sampling is proposed. The basic model is not identified, because class labels are arbitrary. The…

Multidimensional scaling is widely used to handle data that consist of similarity or dissimilarity measures between pairs of objects. We deal with two major problems in metric multidimensional scaling–configuration of objects and determination of the dimension of object configuration–within a Bayesian framework. A Markov chain Monte Carlo algorithm is proposed for object configuration, along with a simple Bayesian criterion, called MDSIC, for choosing their dimension.…

Digital deconvolution concerns the restoration of an underlying discrete signal from a blurred noisy observation sequence. The problem can be formulated in a Bayesian framework. As is usual in the Bayesian context, the computation of relevant posterior quantities is the major challenge. Previous work made substantial progress toward making this computation feasible, most notably through the Gibbs sampling approach of Chen and Li and the sequential importance sampling…