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利用逻辑斯蒂结构直接效应模型估计二元结局的受控直接效应AI 解读

We consider the problem of assessing whether an exposure affects a dichotomous outcome other than by modifying a given mediator. The standard approach, logistic regression adjusting for both exposure and the mediator, is known to be biased in the presence of confounders for the mediator-outcome relationship. Because additional regression adjustment for such confounders is only justified when they are not affected by the exposure, inverse probability weighting has been…

流形上的非参数贝叶斯密度估计及平面形状应用AI 解读

Statistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article…

病例对照研究中前瞻与回顾似然方法的等价性AI 解读

We present new approaches to analyzing case-control studies using prospective likelihood methods. In the classical framework, we extend the equality of the profile likelihoods to the Barndorff-Nielsen modified profile likelihoods for prospective and retrospective models. This enables simple and accurate approximate conditional inference for stratified case-control studies of moderate stratum size. In the Bayesian framework, we provide sufficient conditions on priors for the…

非齐次平面泊松过程强度的 Voronoi 估计量AI 解读

The Voronoi estimator may be defined for any location as the inverse of the area of the corresponding Voronoi cell. We investigate the statistical properties of this estimator for the intensity of an inhomogeneous Poisson process, and demonstrate it is approximately unbiased with a gamma sampling distribution. We also introduce the centroidal Voronoi estimator, a simple extension based on spatial regularization of the point pattern. Simulations show the Voronoi estimator has…

参数极值的 Bootstrap 置信区间与假设检验AI 解读

The bootstrap provides effective and accurate methodology for a wide variety of statistical problems which might not otherwise enjoy practicable solutions. However, there still exist important problems where standard bootstrap estimators are not consistent, and where alternative approaches, for example the m-out-of-n bootstrap and asymptotic methods, also face significant challenges. One of these is the problem of constructing confidence intervals or hypothesis tests for…

不交叉分位数回归曲线估计AI 解读

Since quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution for the response. A simple constrained version of quantile regression is proposed to avoid the crossing problem for both linear and nonparametric quantile curves. A simulation study and a reanalysis of tropical cyclone intensity data shows the usefulness of the procedure. Asymptotic properties of the estimator are equivalent to the typical approach…

非齐次空间点过程的加权估计方程方法AI 解读

We introduce a new estimation method for parametric intensity function models of inhomogeneous spatial point processes based on weighted estimating equations. The weights can incorporate information on both inhomogeneity and dependence of the process. Simulations show that significant efficiency gains can be achieved for non-Poisson processes, compared to the Poisson maximum likelihood estimator. An application to tropical forest data illustrates the use of the proposed…

条件独立模型的边际对数线性参数化AI 解读

Models defined by a set of conditional independence restrictions play an important role in statistical theory and applications, especially, but not only, in graphical modelling. In this paper we identify a subclass of these consisting of hierarchical marginal log-linear models, as defined by Bergsma & Rudas (2002a). Such models are smooth, which implies the applicability of standard asymptotic theory and simplifies interpretation. Furthermore, we give a marginal log-linear…

线性混合模型中边际与条件 AIC 的行为AI 解读

Abstract In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion, aic, have been used, based either on the marginal or on the conditional distribution. We show that the marginal aic is not an asymptotically unbiased estimator of the Akaike information, and favours smaller models without random effects. For the conditional aic, we show that ignoring estimation uncertainty in the random…

删失数据的变系数变换模型AI 解读

A maximum likelihood method with spline smoothing is proposed for linear transformation models with varying coefficients. The estimation and inference procedures are computationally easy. Under some regularity conditions, the estimators are proved to be consistent and asymptotically normal. A simulation study using the Stanford transplant data is presented to show that the proposed method performs well with a finite sample and is easy to use in practice.

连续统回归的若干认识及其渐近性质AI 解读

Continuum regression encompasses ordinary least squares regression, partial least squares regression and principal component regression under the same umbrella using a nonnegative parameter γ. However, there seems to be no literature discussing the asymptotic properties for arbitrary continuum regression parameter γ. This article establishes a relation between continuum regression and sufficient dimension reduction and studies the asymptotic properties of continuum…

惩罚化高维经验似然AI 解读

We propose penalized empirical likelihood for parameter estimation and variable selection for problems with diverging numbers of parameters. Our results are demonstrated for estimating the mean vector in multivariate analysis and regression coefficients in linear models. By using an appropriate penalty function, we showthat penalized empirical likelihood has the oracle property. That is, with probability tending to 1, penalized empirical likelihood identifies the true model…

含协变量模型的参数冗余判定AI 解读

We show how to determine the parameter redundancy status of a model with covariates from that of the same model without covariates, thereby simplifying the calculation considerably. A matrix decomposition is necessary to ensure that the symbolic computation computer programmes return correct results. The paper is illustrated by mark-recovery and latent-class models, with associated Maple code.

基于逆概率加权的有界、高效与双重稳健估计AI 解读

Consider estimating the mean of an outcome in the presence of missing data or estimating population average treatment effects in causal inference. A doubly robust estimator remains consistent if an outcome regression model or a propensity score model is correctly specified. We build on a previous nonparametric likelihood approach and propose new doubly robust estimators, which have desirable properties in efficiency if the propensity score model is correctly specified, and…

存在信息性终止事件的重复事件半参数加性率模型AI 解读

We propose a semiparametric additive rate model for modelling recurrent events in the presence of a terminal event. The dependence between recurrent events and terminal event is nonparametric. A general transformation model is used to model the terminal event. We construct an estimating equation for parameter estimation and derive the asymptotic distributions of the proposed estimators. Simulation studies demonstrate that the proposed inference procedure performs well in…

间接推断的高精度与稳健检验AI 解读

In this paper we propose accurate parameter and over-identification tests for indirect inference. Under the null hypothesis the new tests are asymptotically χ2-distributed with a relative error of order n−1. They exhibit better finite sample accuracy than classical tests for indirect inference, which have the same asymptotic distribution but an absolute error of order n−1/2. Robust versions of the tests are also provided. We illustrate their accuracy in nonlinear regression,…

稀疏高维有向无环图估计的惩罚似然方法AI 解读

Directed acyclic graphs are commonly used to represent causal relationships among random variables in graphical models. Applications of these models arise in the study of physical and biological systems where directed edges between nodes represent the influence of components of the system on each other. Estimation of directed graphs from observational data is computationally NP-hard. In addition, directed graphs with the same structure may be indistinguishable based on…

自回归移动平均方程的严格平稳解AI 解读

Necessary and sufficient conditions for the existence of a strictly stationary solution of the equations defining an autoregressive moving average process driven by an independent and identically distributed noise sequence are determined. No moment assumptions on the driving noise sequence are made.

含多维且部分缺失疾病分类信息的队列研究分析AI 解读

Complex diseases like cancers can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effect of the covariates by the levels of the different disease traits. For inference…

多条序列中共同变点的检测AI 解读

We discuss the detection of local signals that occur at the same location in multiple one-dimensional noisy sequences, with particular attention to relatively weak signals that may occur in only a fraction of the sequences. We propose simple scan and segmentation algorithms based on the sum of the chi-squared statistics for each individual sample, which is equivalent to the generalized likelihood ratio for a model where the errors in each sample are independent. The simple…

边界问题下伪似然比检验统计量的渐近行为AI 解读

This paper considers the asymptotic distribution of the likelihood ratio statistic T for testing a subset of parameter of interest θ, θ = (γ, η), H(0) : γ = γ(0), based on the pseudolikelihood L(θ, ϕ̂), where ϕ̂ is a consistent estimator of ϕ, the nuisance parameter. We show that the asymptotic distribution of T under H(0) is a weighted sum of independent chi-squared variables. Some sufficient conditions are provided for the limiting distribution to be a chi-squared…

二维形状分析的经验似然方法AI 解读

We consider empirical likelihood for the mean similarity shape of objects in two dimensions described by labelled landmarks. The restriction to two dimensions permits the representation of preshapes as complex unit vectors. We focus on the use of empirical likelihood techniques for the construction of confidence regions for the mean shape and for testing the hypothesis of a common mean shape across several populations. Theoretical properties and computational details are…

单调回归的分组 Brunk 估计与惩罚样条估计AI 解读

We study a class of monotone univariate regression estimators. We use B-splines to approximate an underlying regression function and estimate spline coefficients based on grouped data. We investigate asymptotic properties of two monotone estimators: a grouped Brunk estimator and a penalized monotone estimator. These estimators are consistent at the boundary and their mean square errors achieve optimal convergence rates under suitable assumptions of the true regression…

嵌套采样的统计性质AI 解读

Nested sampling is a simulation method for approximating marginal likelihoods. We establish that nested sampling has an approximation error that vanishes at the standard Monte Carlo rate and that this error is asymptotically Gaussian. It is shown that the asymptotic variance of the nested sampling approximation typically grows linearly with the dimension of the parameter. We discuss the applicability and efficiency of nested sampling in realistic problems, and compare it…