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物种数量估计：综述AI 解读

How many kinds are there? Suppose that a population is partitioned into C classes. In many situations interest focuses not on estimation of the relative sizes of the classes, but on estimation of C itself. For example, biologists and ecologists may be interested in estimating the number of species in a population of plants or animals, numismatists may be concemed with estimating the number of dies used to produce an ancient coin issue, and linguists may be interested in…

广义线性模型中的随机效应预测AI 解读

This article develops an estimating function-based approach to component estimation in the two-stage generalized linear model with univariate random effects and a vector of fixed effects. The novelty and unifying feature of the method is the use of estimating functions in the estimation of both the random effects and their variance. Two separate estimating procedures based on the method are proposed that differ in the intensity of numerical computation required. The…

多变量不完整数据的模式混合模型AI 解读

Consider a random sample on variables X1, …, Xv with some values of Xv missing. Selection models specify the distribution of X1 , …, XV over respondents and nonrespondents to Xv , and the conditional distribution that Xv is missing given X1 , …, Xv . In contrast, pattern-mixture models specify the conditional distribution of X 1, …, Xv given that XV is observed or missing respectively and the marginal distribution of the binary indicator for whether or not Xv is missing. For…

变点问题的贝叶斯分析AI 解读

A sequence of observations undergoes sudden changes at unknown times. We model the process by supposing that there is an underlying sequence of parameters partitioned into contiguous blocks of equal parameter values; the beginning of each block is said to be a change point. Observations are then assumed to be independent in different blocks given the sequence of parameters. In a Bayesian analysis it is necessary to give probability distributions to both the change points and…

从置信区间进行贝叶斯与似然计算AI 解读

Recently there has been considerable progress on setting good approximate confidence intervals for a single parameter θ in a multi-parameter family. Here we use these frequentist results as a convenient device for making Bayes, empirical Bayes and likelihood inferences about θ. A simple formula is given that produces an approximate likelihood function Lx†(θ) for θ, with all nuisance parameters eliminated, based on any system of approximate confidence intervals. The…

改善贝叶斯推断参数化的诊断图AI 解读

A major problem with the Bayesian analysis of statistical models is that the computation of posterior and predictive summaries typically requires the numerical evaluation of complicated multivariate integrals. The accuracy and efficiency of these computations are usually sensitive to the choice of parameterization for the statistical model. This paper describes a graphical method which can be used both for assessing a proposed parameterization and choosing a new improved…

摘要暂缺

同时具有置信区间性质的贝叶斯区间估计AI 解读

SUMMARY Let Y 1, . . ., Yn denote independent observations each distributed according to a distribution depending on a scalar parameter θ suppose that we are interested in constructing an interval estimate for θ. One approach is to use Bayesian inference. For a given prior density, we can construct an interval such that the posterior probability that θ lies in the interval is some specified value. In this paper, a method is given for constructing a Bayesian interval estimate…

用混合分布近似后验分布AI 解读

SUMMARY Kernel density estimation techniques are used to smooth simulated samples from importance sampling function approximations to posterior distributions, resulting in revised approximations that are mixtures of standard parametric forms, usually multivariate normal or T-distributions. Adaptive refinement of such mixture approximations involves repeating this process to home-in successively on the posterior. In fairly low dimensional problems, this provides a general and…

两阶段整群抽样下有限总体比例的贝叶斯预测推断AI 解读

SUMMARY Given binary data from a two-stage cluster sample, we present a method to carry out Bayesian predictive inference for a finite population proportion. Our probabilistic specification should be useful for many surveys of this type and yields simple analytical expressions for the prior and posterior mean and variance. Within cluster k, we assume that the Yki are a random sample from the Bernoulli distribution with probability θk. Conditional on β and τ, θ1,..., θN are a…

存在干扰参数时频率表现良好的贝叶斯区间AI 解读

SUMMARY Given a random sample from a distribution with density function that depends on an unknown parameter θ = (θ1 . . ., θp), we are concerned with the problem of setting confidence intervals for a particular component of it, say θ1, treating the remaining components as a nuisance parameter. Adopting an objective Bayesian approach, we show that the Bayes intervals with a certain conditional prior density on the parameter of interest, θ1, are confidence intervals as well,…

均值与方差发生马尔可夫状态切换的自回归时间序列：Gibbs 抽样贝叶斯推断AI 解读

The authors examine autoregressive time series models subject to regime switching. A Bayesian framework is develope d in which the unobserved.states, one for each time point, are treated as missing data and then analyzed using the Gibbs sampler. This approac h is straightforward because the conditional posterior distribution of the parameters given the states, and the conditional posterior distribution of the states given the parameters, are amenable to simulation. The…

近似贝叶斯推断中的 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…

线性模型异常值检测概率方法的比较AI 解读

This paper compares the use of two posterior probability methods to deal with outliers in linear models. We show that putting together diagnostics that come from the mean-shift and variance-shift models yields a procedure that seems to be more effective than the use of probabilities computed from the posterior distributions of actual realized residuals. The relation of the suggested procedure to the use of a certain predictive distribution for diagnostics is derived.

具有光滑随机效应密度的非线性混合效应模型AI 解读

The fixed parameters of the nonlinear mixed effects model and the density of the random effects are estimated jointly by maximum likelihood. The density of the random effects is assumed to be smooth but is otherwise unrestricted. The method uses a series expansion that follows from the smoothness assumption to represent the density and quadrature to compute the likelihood. Standard algorithms are used for optimization. Empirical Bayes estimates of random coefficients are…

存在干扰参数时后验分位数的频率学有效性：高阶渐近分析AI 解读

Given a random sample from a distribution with density function that depends on an unknown parameter θ = (θ1, θ2), we are concerned with frequentist validity, up to o(n−1), of posterior quantiles of θ1, treating θ2 as a nuisance parameter. We propose to make the best choice of the prior on θ by matching, as far as practicable, the posterior and frequentist coverage probabilities up to o(n−1).

效力曲线形状先验约束下的非参数贝叶斯生物测定AI 解读

We consider the problem of implementing nonparametric Bayesian analysis of bioassay problems when the prior constrains the form of the potency curve (for example, to be convex, concave or ogive). Sampling based approaches to calculating posterior features of interest are detailed and illustrated. Extensions to prior constraints on hazard rates are outlined.

lq球对称模型中的稳健贝叶斯推断AI 解读

The class of multivariate lq-spherical distributions is introduced and defined through their isodensity surfaces. We prove that, under a Jeffrey's type improper prior on the scale parameter, posterior inference on the location parameters is the same for all lq-spherical sampling models with common q. This gives us perfect inference robustness with respect to any departures from the reference case of independent sampling from the exponential power distribution.

部分可交换性与调查抽样推断AI 解读

The role of the sampling mechanism for Bayesian inference on symmetric functions of a finite population is examined under partial design and partial label information. A new definition of partial exchangeability is proposed, together with a corresponding property of the sampling mechanism. These properties provide conditions under which certain symmetries in priors or designs can protect against lack of symmetry in others, in the sense of Scott & Smith (1973).

利用参考先验消除干扰参数AI 解读

The problem of eliminating nuisance parameters is tackled from different points of view. Standard likelihood techniques, such as profile likelihood and its modifications, are compared with a Bayesian analysis based on reference priors (Berger & Bernardo, 1992a). Examples are considered to illustrate what happens in multiparameter problems. In particular the frequentist coverage probabilities of the credible sets derived from the reference priors are shown to be better than…

用于改进贝叶斯推断参数化的诊断图AI 解读

A major problem with the Bayesian analysis of statistical models is that the computation of posterior and predictive summaries typically requires the numerical evaluation of complicated multivariate integrals. The accuracy and efficiency of these computations are usually sensitive to the choice of parameterization for the statistical model. This paper describes a graphical method which can be used both for assessing a proposed parameterization and choosing a new improved…

由置信区间进行贝叶斯与似然计算AI 解读

Recently there has been considerable progress on setting good approximate confidence intervals for a single parameter θ in a multi-parameter family. Here we use these frequentist results as a convenient device for making Bayes, empirical Bayes and likelihood inferences about θ. A simple formula is given that produces an approximate likelihood function Lx†(θ) for θ, with all nuisance parameters eliminated, based on any system of approximate confidence intervals. The…

高斯白噪声观测下跳跃马尔可夫过程的一种滤波方法AI 解读

The importance of optimal filtration problem for Markov chain with two states observed in Gaussian white noise (GWN) for a lot of concrete technical problems is well known. The equation for a posterior probability $\pi(t)$ of one of the states was obtained many years ago. The aim of this paper is to study a simple filtration method. It is shown that this simplified filtration is asymptotically efficient in some sense if the diffusion constant of the GWN goes to 0. Some…

有限集合上的不确定概率AI 解读

This paper presents a quasi-Bayesian model of subjective uncertainty in which beliefs which are represented by lower and upper probabilities qualified by numerical confidence weights. The representation is derived from a system of axioms of binary preferences which differs from standard axiom systems insofar as completeness is not assumed and transitivity is weakened. Confidence-weighted probabilities may be elicited through the acceptance of bets with limited stakes, a…