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非正态分布下简单随机效应模型的估计AI 解读

A simple structural model is considered involving the addition of two random variables representing between‐ and within‐group variation. Methods for estimating the cumulants of the two components of variation are proposed, based on homogeneous polynomials in the data. Emphasis is placed on situations in which the number of observations per group is quite small. In some cases an essentially unique estimator is available, whereas in others there is a family of possible…

计算机模型输出不确定性分布的贝叶斯推断AI 解读

We consider a problem of inference for the output of a computationally expensive computer model. We suppose that the model is to be used in a context where the values of one or more inputs are uncertain, so that the input configuration is a random variable. We require to make inference about the induced distribution of the output. This distribution is called the uncertainty distribution, and the general problem is known to users of computer models as uncertainty analysis. To…

通过条件矩方程渐近逼近后验分布AI 解读

We consider asymptotic approximations to joint posterior distributions in situations where the full conditional distributions referred to in Gibbs sampling are asymptotically normal. Our development focuses on problems where data augmentation facilitates simpler calculations, but results hold more generally. Asymptotic mean vectors are obtained as simultaneous solutions to fixed point equations that arise naturally in the development. Asymptotic covariance matrices flow…

马尔可夫随机场的递归算法AI 解读

We propose a recursive algorithm as a more useful alternative to the Brook expansion for the joint distribution of a vector of random variables when the original formulation is in terms of the corresponding full conditional distributions, as occurs for Markov random fields. Usually, in practical applications, the computational load will still be excessive but then the algorithm can be used to obtain the componentwise full conditionals of a system after marginalising over…

兼顾线性模型辨别与参数估计的有效试验设计AI 解读

We consider experimental designs in a regression set‐up where the unknown regression function belongs to a known family of nested linear models. The objective of our design is to select the correct model from the family of nested models as well as to estimate efficiently the parameters associated with that model. We show that our proposed design is able to choose the true model with probability tending to one as the number of trials grows to infinity. We also establish that…

圆周回归AI 解读

A new model for an angular regression link function is introduced. The model employs an angular scale parameter, incorporates proper and improper rotations as special cases, and is equivalent to the Möbius circle mapping for complex variables. Desirable properties of the circle mapping carry over to angular regression. Parameter estimation and inferential methods are developed and illustrated.

删失数据变换模型的半参数分析AI 解读

A unified estimation procedure is proposed for the analysis of censored data using linear transformation models, which include the proportional hazards model and the proportional odds model as special cases. This procedure is easily implemented numerically and its validity does not rely on the assumption of independence between the covariates and the censoring variable. The estimator is the same as the Cox partial likelihood estimator in the case of the proportional hazards…

现状数据下加性风险回归的有效估计AI 解读

Current status data arise when the exact timing of an event is unobserved, and it is only known at a given point in time whether or not the event has occurred. Recently Lin et al. (1998) studied the additive semiparametric hazards model for current status data. They showed that the analysis of current status data under the additive hazards model reduces to ordinary Cox regression under the assumption that a proportional hazards model may be used to describe the monitoring…

信息性删失下失效时间分布的估计AI 解读

We present a method for estimating the survival curve of a continuous failure time random variable from right-censored data. Our method allows adjustment for informative censoring due to measured prognostic factors for time-to-event and censoring while simultaneously quantifying the sensitivity of the inference to residual dependence between failure and censoring due to unmeasured factors. We present the results of a simulation study and illustrate our approach using data…

依赖结构未指定的纵向数据半参数模型估计AI 解读

This paper considers an extension of M‐estimators in semiparametric models for independent observations to the case of longitudinal data. We approximate the nonparametric function by a regression spline, and any M‐estimation algorithm for the usual linear models can then be used to obtain consistent estimators of the model and valid large‐sample inferences about the regression parameters without any specification of the error distribution and the covariance structure.…

聚类数据分析：组合估计方程方法AI 解读

Examples of clustered data include data from longitudinal studies and data sampled within groups. This paper proposes a regression analysis method for clustered data that optimally weights and combines contrasts of the data through a combination of estimating equations. Examples of combining between‐cluster, within‐cluster and longitudinal data contrasts are presented. The method results in increased estimation efficiency relative to generalised estimating equations with…

纵向数据协方差矩阵与动态模型的贝叶斯分析AI 解读

Parsimonious modelling of the within‐subject covariance structure while heeding its positive‐definiteness is of great importance in the analysis of longitudinal data. Using the Cholesky decomposition and the ensuing unconstrained and statistically meaningful reparameterisation, we provide a convenient and intuitive framework for developing conditionally conjugate prior distributions for covariance matrices and show their connections with generalised inverse Wishart priors.…

摘要暂缺

可忽略无响应下部分观测数据的贝叶斯非参数多重插补AI 解读

We present a new, nonparametric Bayesian method for multiple imputation of partially observed data for which the pattern of missingness is arbitrary and the data are missing at random with ignorable nonresponse with respect to the model specification. Motivation for the method is provided, followed by an overview of Pólya trees and their application to multiple imputation, and a comparison of the new method to existing approaches is presented. The method is illustrated on a…

用于局部自适应非参数回归的贝叶斯样条混合AI 解读

A Bayesian approach is presented for spatially adaptive nonparametric regression where the regression function is modelled as a mixture of splines. Each component spline in the mixture has associated with it a smoothing parameter which is defined over a local region of the covariate space. These local regions overlap such that individual data points may lie simultaneously in multiple regions. Consequently each component spline has attached to it a weight at each point of the…

用于模型选择的期望后验先验分布AI 解读

We consider the problem of comparing parametric models using a Bayesian approach. A new method of developing prior distributions for the model parameters is presented, called the expected‐posterior prior approach. The idea is to define the priors for all models from a common underlying predictive distribution, in such a way that the resulting priors are amenable to modern Markov chain Monte Carlo computational techniques. The approach has subjective Bayesian and default…

两个相依圆周变量的概率模型AI 解读

Motivated by problems in molecular biology and molecular physics, we propose a five‐parameter torus analogue of the bivariate normal distribution for modelling the distribution of two circular random variables. The conditional distributions of the proposed distribution are von Mises. The marginal distributions are symmetric around their means and are either unimodal or bimodal. The type of shape depends on the configuration of parameters, and we derive the conditions that…

基于最大似然排序的停止后追加数据序贯检验与估计AI 解读

SUMMARY Often in sequential trials some additional data become available after a stopping boundary has been reached. A method for incorporating such information from overrunning is developed, based on a maximum-likelihood ordering of the sample space after overrunning. This yields a p-value for the primary test and a median-unbiased estimator and confidence intervals for the parameter under test. The context is that of observing a Brownian motion with drift, with either…

覆盖问题的 Poisson 模型及其基因组应用AI 解读

Suppose a population has infinitely many individuals and is partitioned into unknown N disjoint classes. The sample coverage of a random sample from the population is the total proportion of the classes observed in the sample. This paper uses a nonparametric Poisson mixture model to give new understanding and results for inference on the sample coverage. The Poisson mixture model provides a simplified framework for inferring any general abundance‐K coverage, the sum of the…

非嵌套 Cox 模型的比较AI 解读

We derive the limiting distribution of the partial likelihood ratio under general conditions. The multiplicative hazards models being fitted may be nonnested and misspecified. The true model is not assumed to contain either model under consideration. The null hypothesis is that the models are equidistant in Kullback–Leibler metric applied to the rank likelihood. The statistic is consistent for the model which is closer to the truth. Its distribution depends on the unknown…

状态空间时间序列分析的简便高效模拟平滑器AI 解读

A simulation smoother in state space time series analysis is a procedure for drawing samples from the conditional distribution of state or disturbance vectors given the observations. We present a new technique for this which is both simple and computationally efficient. The treatment includes models with diffuse initial conditions and regression effects. Computational comparisons are made with the previous standard method. Two applications are provided to illustrate the use…

具有相互相关性的动态面板模型适合性检验AI 解读

We give several definitions of residual autocorrelations and derive their joint asymptotic distribution for the panel time series model of Hjellvik & Tjøstheim (1999a). A portmanteau goodness‐of‐fit test arises naturally from the asymptotic distribution. Simulation results show that the asymptotic standard errors compared satisfactorily with the empirical standard errors, that the goodness‐of‐fit test has reasonable empirical size, and that it is powerful enough to be useful…

摘要暂缺

近似贝叶斯自助法插补的一则说明AI 解读

The approximate Bayesian bootstrap is suggested by Rubin & Schenker (1986) as a way of generating multiple imputations when the original sample can be regarded as independently and identically distributed and the response mechanism is ignorable. We investigate the finite sample properties of the variance estimator when the approximate Bayesian bootstrap method is used and show that the bias is not negligible for moderate sample sizes. A modification of the method is proposed…