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一般条件下的两样本分位数检验AI 解读

A simple, nonparametric two-sample test for equality of a given collection of quantiles is developed which can be applied to a variety of empirical distribution functions, including the Kaplan-Meier estimator, a self-consistent estimator for doubly-censored data and an estimator for repeated measures data. The null hypothesis tested is that the quantiles are equal but other aspects of the distributions may differ between the two samples. This procedure can also be applied to…

部分共同主成分子空间AI 解读

We consider the principal components analysis of g groups of m variables for those situations in which, for each group, the first k principal components account for most of the total variability of observations in that group. If the set of these gk principal component vectors spans a space of dimension r, where r is less than m, then it will be possible simultaneously to reduce the dimensionality, for all groups, from m to r while retaining most of the within-group…

基于预测似然或自助法的预测区间AI 解读

We argue that prediction intervals based on predictive likelihood do not correct for curvature with respect to the parameter value when they implicitly approximate an unknown probability density. Partly as a result of this difficulty, the order of coverage error associated with predictive intervals and predictive limits is equal to only the inverse of sample size. In this respect those methods do not improve on the simpler, 'naive' or 'estimative' approach. Moreover, in…

降低半参数模型对滋扰参数的敏感性：拟得分方法AI 解读

This paper proposes a semiparametric extension of the projected score method of Waterman & Lindsay (1996) for the elimination of nuisance parameters. The procedure addresses cases where only the mean and the variance of the response variable are specified and where the mean function involves both parameters of interest and nuisance parameters. Important applications of the semiparametric model include quasilikelihood models for matched designs and for measurement error…

二元回归中响应误分类导致的偏差与效率损失AI 解读

Methods that ignore errors in binary responses yield biased estimators of the associations of covariates with response. This paper derives general expressions for the magnitude of the bias due to errors in the response and shows that, unless both the sensitivity and specificity are very high, ignoring errors in the responses will yield highly biased covariate effect estimators. When the true, error-free response follows a generalised linear model and misclassification…

扩展的部分线性单指标模型AI 解读

Aiming to explore the relation between the response y and the stochastic explanatory vector variable X beyond the linear approximation, we consider the single-index model, which is a well-known approach in multidimensional cases. Specifically, we extend the partially linear single-index model to take the form y=βT0X + φ(θT0X) + ε, where ε is a random variable with Εε=0 and var(ε)=σ2, unknown, β0 and θ0 are unknown parametric vectors and φ(.) is an unknown real function. The…

超越接受—拒绝抽样AI 解读

We propose a new estimator for estimating a quantity μ via Monte Carlo simulations, where μ=∫ƒdP and P is the target probability measure. The new estimator outperforms the usual accept-reject algorithm in terms of reducing the variance. Properties of the estimator are derived. A new Rao-Blackwellised version of the estimator is also produced, but this version requires enormous computing time. Numerical results are provided.

论高效概率预测系统AI 解读

We study the asymptotic behaviour of probability forecasting systems, and discuss their usefulness as inferential tools for statistical problems such as model verification and selection. Our theoretical setting is the prequential, or predictive sequential, framework proposed by Dawid (1984). We study especially the notion of prequential efficiency of a forecasting system and present some new results. We focus on plug-in, or estimative, forecasting systems, where the forecast…

多重插补中的小样本自由度调整AI 解读

An appealing feature of multiple imputation is the simplicity of the rules for combining the multiple complete-data inferences into a final inference, the repeated-imputation inference (Rubin, 1987). This inference is based on a t distribution and is derived from a Bayesian paradigm under the assumption that the complete-data degrees of freedom, νcom, are infinite, but the number of imputations, m, is finite. When νcom is small and there is only a modest proportion of…

杂论：密度估计前的数据锐化AI 解读

We introduce a data-perturbation method for reducing bias of a wide variety of density estimators, in univariate, multivariate, spatial and spherical data settings. The method involves 'sharpening' the data by making them slightly more clustered than before, and then computing the estimator in the usual way, but from the sharpened data rather than the original data. The transformation depends in a simple, explicit way on the smoothing parameter employed for the density…

杂记：惩罚样条回归的最优平滑程度AI 解读

The optimal amount of smoothing in penalised spline regression is investigated. In particular, a simple closed form approximation to the optimal smoothing parameter is derived. Comparisons with its exact counterpart show it to be a useful starting point for measuring the optimal amount of smoothing in penalised spline regression. It also lends itself to the development of quick and simple rules for automatic smoothing parameter selection.

杂录：正态变量二次型分布的鞍点近似AI 解读

An extensive study and several applications show that a saddlepoint approximation for the distribution of quadratic forms in normal variates works very well and outperforms existing approximate methods in accuracy.

回顾性抽样问题的高阶精确方法AI 解读

In this paper we discuss the relationship between prospective and retrospective sampling problems. Estimates of the parameter of interest may be obtained by solving suitable estimating equations under both sampling schemes. Most common examples of such estimates include the maximum likelihood estimates. Some classical results and more recent development of the first-order asymptotic relationship between the estimators are reviewed. High-order expansions are given for the…

时空卡尔曼滤波的降维方法AI 解读

Many physical/biological processes involve variability over both space and time. As a result of difficulties caused by large datasets and the modelling of space, time and spatio-temporal interactions, traditional space-time methods are limited. In this paper, we present an approach to space-time prediction that achieves dimension reduction and uses a statistical model that is temporally dynamic and spatially descriptive. That is, it exploits the unidirectional flow of time,…

可分解高斯图模型的贝叶斯模型选择AI 解读

We propose a methodology for Bayesian model determination in decomposable graphical gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obtained by marginalisation from the prior conditional on the complete graph. We explore alternative structures for the hyperparameters of the latter, and their consequences for the…

Cox模型下的病例队列与病例对照分析AI 解读

Prentice (1986) proposed the case-cohort design and studied a pseudolikelihood estimator of regression parameters in Cox's model. We derive a class of estimating equations for case-cohort sampling, each depending on a different estimator of the population distribution, which lead naturally to simple estimators that improve on Prentice's pseudolikelihood estimator. We also discuss an equivalence between case-control and case-cohort sampling in terms of the estimation of…

Correlated failure time data arise routinely in case-control family studies. To measure the dependence between bivariate failure times, a cross ratio, i.e. conditional hazard ratio, function has been used. Many approaches, ranging from estimating equations to likelihood score equations, have been proposed for the estimation of parameters in the cross ratio function from prospective family data. However, the adaptation of these approaches to case-control family data appears…

含大量干扰参数模型中的定向修正剖面似然AI 解读

The modified profile likelihood is a higher-order approximation to a conditional or a marginal likelihood, when either exists. The directed likelihood based on the modified profile likelihood is standard normal only to first order. In practice, however, it usually achieves most of the distributional improvement obtained by higher-order methods. This note elucidates this point with particular attention to models with many nuisance parameters.

杂录：用于多项聚类数据的 Morel–Nagaraj 有限混合分布扩展AI 解读

Morel & Nagaraj (1993) proposed a finite mixture distribution for modelling multinomial extra variation when the extra variation is believed to be caused by clumped multinomial sampling with one clumping. We propose an extension of the model to incorporate possible multiple clumping. We show that the extended finite mixture distribution is a multinomial mixing distribution with different mixing coefficients. We derive maximum likelihood estimating equations and an asymptotic…

杂论：纵向数据的随机系数自回归模型AI 解读

Stable autoregressive models with exogenous covariates can be used to mimic nonstationary, stabilising growth profiles in longitudinal data. If different individuals have very different trajectories, the autoregressive parameters can be interpreted as random variables varying from one individual to the next. If all random effects are assumed Gaussian, the model with be quite easy to estimate although the marginal distributions of the observations will no longer be Gaussian.…

杂论：分位数估计与马尔可夫链蒙特卡洛收敛AI 解读

In this paper we examine the method of Raftery & Lewis (1992) for estimating the convergence of Markov chain Monte Carlo, samplers when functionals of interest are in the form of parameter quantiles. Although popular, the method is commonly mis-applied to problems where quantiles are not of primary interest. We show how the method can be misleading in this case, and that it can seriously underestimate the true length of the burn-in. We provide a number of examples, comparing…

杂论：正值变量的一类多元分布AI 解读

In the context of parametric survival analysis, it is necessary to specify probability distributions on (0, ∞). Typically, the exponential, Weibull, gamma, Pareto or log-normal is used. However, attempts to generalise these distributions to a multivariate setting have proved problematic. This paper introduces a univariate family of distributions, called the beta-log-normal family, motivated by a mixture representation of some of the more typical distributions and which…

纵向数据的半参数加性回归模型AI 解读

In previous work we have studied a nonparametric additive time-varying regression model for longitudinal data recorded at irregular intervals. The model allows the influence of each covariate to vary separately with time. For small datasets, however, only a limited number of covariates may be handled in this way. In this paper, we introduced a semiparametric regression model for longitudinal data. The influence of some of the covariates varies nonparametrically with time…

纵向数据的均值—协方差联合模型：无约束参数化AI 解读

We provide unconstrained parameterisation for and model a covariance using covariates. The Cholesky decomposition of the inverse of a covariance matrix is used to associate a unique unit lower triangular and a unique diagonal matrix with each covariance matrix. The entries of the lower triangular and the log of the diagonal matrix are unconstrained and have meaning as regression coefficients and prediction variances when regressing a measurement on its predecessors. An…