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EM 算法的多重插补 Metropolis 版本AI 解读

In this paper we introduce a new stochastic variant of the EM algorithm. The algorithm combines the principle of multiple imputation and the theory of simulated annealing to deal with cases where the E‐step and the M‐step can be intractable or numerically inefficient.

由 Dabrowska 关联度量生成的 copula 模型AI 解读

We propose a new archimedean copula model for bivariate survival data that is motivated by Dabrowska's (1988) measure of association. The model can represent negatively correlated or moderately positively correlated data but not highly positively correlated data. Local and global measures of association are calculated. A generalisation is presented.

利用合并检测估计有序二项比例AI 解读

This paper considers group testing when the probability of response is increasing across the levels of an observed covariate. We illustrate how previously known results in order‐restricted inference can be extended to situations wherein data are collected according to a group‐testing protocol, and we derive maximum likelihood estimators for proportions under the increasing order restriction and group‐testing model. Finally, we show how the use of group testing can…

Wilson–Hilferty 变换的局部鞍点性质AI 解读

Interest in transform methods for normalising test statistics declined with the advent of computers. More recently, small‐sample asymptotic methods have been developed to approximate the distributions of complicated test statistics. We propose a generalisation of a classical symmetrising transform to a similar range of statistics. It is shown to behave comparably in a parametric neighbourhood to methods that exploit exponential tilting.

部分共同主成分子空间的加权卡方检验AI 解读

We consider tests of the null hypothesis that g covariance matrices have a partial common principal component subspace of dimension s. Our approach uses a dimensionality matrix which has its rank equal to s when the hypothesis holds. The test can then be based on a statistic computed from the eigenvalues of an estimate of this dimensionality matrix. The asymptotic distribution of this statistic is that of a linear combination of independent one‐degree‐of‐freedom chi‐squared…

纵向计数数据的序列相关伽马脆弱性模型AI 解读

A Poisson‐gamma model is introduced to account for between‐subjects heterogeneity and within‐subjects serial correlation occurring in longitudinal count data. The model extends the usual time‐constant shared frailty approach to allow time‐varying serially correlated gamma frailty whilst retaining standard marginal assumptions. A composite likelihood approach to estimation and testing for serial correlation is proposed. The work is motivated by a clinical trial on…

分量参数的似然AI 解读

For a statistical model with data, likelihood for the scalar or vector full parameter θ, of dimension p say, is typically well defined and easily computed. In this paper, we investigate likelihood for a component parameter ψ(θ) of dimension d < p and make use of the recent likelihood theory that has been successful in producing highly accurate third‐order p‐values for scalar parameters of continuous models. The theory leads under moderate regularity to a definitive…

完全贝叶斯样条平滑与内蕴自回归先验AI 解读

There is a well‐known Bayesian interpretation for function estimation by spline smoothing using a limit of proper normal priors. The limiting prior and the conditional and intrinsic autoregressive priors popular for spatial modelling have a common form, which we call partially informative normal. We derive necessary and sufficient conditions for the propriety of the posterior for this class of partially informative normal priors with noninformative priors on the variance…

多元时间序列图模型的可分解性与模型选择AI 解读

We derive conditions for decomposition and collapsibility of graphical interaction models for multivariate time series. These properties enable us to perform stepwise model selection under certain restrictions. For illustration, we apply the results to a multivariate time series describing the haemodynamic system as monitored in intensive care.

用于模拟指定均值与相关性的多元二元分布族AI 解读

We introduce a family of multivariate binary distributions with certain conditional linear property. This family is particularly useful for efficient and easy simulation of correlated binary variables with a given marginal mean vector and correlation matrix.

Arnason–Schwarz 模型的闭式似然AI 解读

We provide a general framework for the computationally efficient analysis, both Bayesian and classical, of integrated multi‐site recovery/recapture models in the presence of individual‐level covariates by extending the basic Arnason–Schwarz models and deriving closed‐form likelihood expressions, together with corresponding sufficient statistics.

通过加权自助法迭代进行预枢轴化AI 解读

Prepivoting by conventional bootstrap iteration is known to yield a progressively more accurate pivot in certain problems, and has important application in the construction of confidence limits and estimation of null distributions. We investigate the theoretical effects of weighted bootstrap iteration on prepivoting and show that each weighted bootstrap iteration, with weights chosen carefully but empirically, is asymptotically equivalent to two consecutive conventional…

基于半参数模型的判别分析AI 解读

We consider a semiparametric generalisation of normal‐theory discriminant analysis. The semiparametric model assumes that, after unspecified univariate monotone transformations, the class distributions are multivariate normal. We introduce an estimation procedure based on the distribution quantiles, in which the parameters of the semiparametric model are estimated directly without estimating the nonparametric transformations. The procedure is computationally fast and the…

临床试验监测中自适应设计的低效率问题AI 解读

Adaptive designs, which allow the sample size to be modified based on sequentially computed observed treatment differences, have been advocated recently for monitoring clinical trials. Although such methods have a great deal of appeal on the surface, we show that such methods are inefficient and that one can improve uniformly on such adaptive designs using standard group‐sequential tests based on the sequentially computed likelihood ratio test statistic.

加速失效时间模型的秩推断AI 解读

A broad class of rank‐based monotone estimating functions is developed for the semiparametric accelerated failure time model with censored observations. The corresponding estimators can be obtained via linear programming, and are shown to be consistent and asymptotically normal. The limiting covariance matrices can be estimated by a resampling technique, which does not involve nonparametric density estimation or numerical derivatives. The new estimators represent consistent…

贝叶斯经验似然AI 解读

Research has shown that empirical likelihood tests have many of the same asymptotic properties as those derived from parametric likelihoods. This leads naturally to the possibility of using empirical likelihood as the basis for Bayesian inference. Different ways in which this goal might be accomplished are considered. The validity of the resultant posterior inferences is examined, as are frequentist properties of the Bayesian empirical likelihood intervals.

部分随机序约束下的贝叶斯方法AI 解读

We discuss two methods of making nonparametric Bayesian inference on probability measures subject to a partial stochastic ordering. The first method involves a nonparametric prior for a measure on partially ordered latent observations, and the second involves rejection sampling. Computational approaches are discussed for each method, and interpretations of prior and posterior information are discussed. An application is presented in which inference is made on the number of…

删失数据的非参数协方差分析AI 解读

The fully nonparametric model for nonlinear analysis of covariance, proposed in Akritas et al. (2000), is considered in the context of censored observations. Under this model, the distributions for each factor level combination and covariate value are not restricted to comply to any parametric or semiparametric model. The data can be continuous or ordinal categorical. The possibility of different shapes of covariate effect in different factor level combinations is also…

异方差因子分析AI 解读

Two moment‐based model‐fitting procedures for the heteroscedastic factor analysis model are introduced and compared. The procedures produce consistent parameter estimators and asymptotically valid inferences for heteroscedasticity without specifying the distributional forms for the factor and heteroscedastic errors. Also, an individual‐specific inference procedure for the factor score is developed. Simulation studies show the practical usefulness of the procedures. An…

具有恰当时间依赖结构的模式混合模型AI 解读

Recently, pattern‐mixture modelling has become a popular tool for modelling incomplete longitudinal data. Such models are under‐identified in the sense that, for any drop‐out pattern, the data provide no direct information on the distribution of the unobserved outcomes, given the observed ones. One simple way of overcoming this problem, ordinary extrapolation of sufficiently simple pattern‐specific models, often produces rather unlikely descriptions; several authors consider…

工作相关结构误设、参数估计与协变量设计对广义估计方程性能的影响AI 解读

The method of generalised estimating equations for regression modelling of clustered outcomes allows for specification of a working matrix that is intended to approximate the true correlation matrix of the observations. We investigate the asymptotic relative efficiency of the generalised estimating equation for the mean parameters when the correlation parameters are estimated by various methods. The asymptotic relative efficiency depends on three features of the analysis,…

纵向研究中的均值与协方差结构建模AI 解读

We exploit a reparameterisation of the marginal covariance matrix arising in longitudinal studies (Pourahmadi, 1999, 2000) to model, jointly, the mean and covariance structures in terms of three polynomial functions of time. By reanalysing Kenward's (1987) cattle data, we compare model selection procedures based on regressogram estimation with these based on a global search of the model space. Using a BIC‐based model selection criterion to identify the optimum degree triple…

利用形状约束回归检验线性与凸回归函数AI 解读

An unbiased test for the appropriateness of the simple linear regression model is presented. The null hypothesis is that the underlying regression function is indeed a line, and the alternative is that it is convex. The exact distribution for a likelihood ratio test statistic is that of a mixture of beta random variables, with the mixing distribution calculated from relative volumes of polyhedral convex cones determined by the convex shape restriction. Simulations show that…

Bürmann展开与可加性检验AI 解读

We propose a Lagrange multiplier test for additivity based on the Bürmann expansion of a conditional mean function. The asymptotic null distribution of the test is shown to be x2, under some regularity conditions. In contrast, the Lagrange multiplier test proposed by Chen et al. (1995) is based on the Volterra expansion of the conditional mean function. We discuss some desirable advantages of the Bürmann expansion over the Volterra expansion for nonlinear time series…