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区间删失失效时间数据的病例队列研究AI 解读

The case-cohort design has been widely used as a means of cost reduction in assembling or measuring expensive covariates in large cohort studies. The existing literature on the case-cohort design is mainly focused on right-censored data. In practice, however, the failure time is often subject to interval-censoring; it is known only to fall within some random time interval. In this paper, we consider the case-cohort study design for interval-censored failure time and develop…

渐近依赖下的修正边际期望损失AI 解读

We propose an estimator of the marginal expected shortfall by considering a log transformation of a variable which has an infinite expectation.We establish the asymptotic normality of our estimator under general assumptions.A simulation study suggests that the estimation procedure is robust with respect to the choice of tuning parameters.Our estimator has lower bias and mean squared error than the empirical estimator when the latter is applicable.We illustrate our…

利用充分降维估计基于回归的因果效应AI 解读

In many causal inference problems the parameter of interest is the regression causal effect, defined as the conditional mean difference in the potential outcomes given covariates. In this paper we discuss how sufficient dimension reduction can be used to aid causal inference, and we propose a new estimator of the regression causal effect inspired by minimum average variance estimation. The estimator requires a weaker common support condition than propensity score-based…

Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures…

不完整多变量数据的逐项条件独立无应答建模AI 解读

We introduce a nonresponse mechanism for multivariate missing data in which each study variable and its nonresponse indicator are conditionally independent given the remaining variables and their nonresponse indicators. This is a nonignorable missingness mechanism, in that nonresponse for any item can depend on values of other items that are themselves missing. We show that under this itemwise conditionally independent nonresponse assumption, one can define and identify…

分层数据中的 lasso 回归建模AI 解读

We consider the estimation of regression models on strata defined using a categorical covariate, in order to identify interactions between this categorical covariate and the other predictors. A basic approach requires the choice of a reference stratum. We show that the performance of a penalized version of this approach depends on this arbitrary choice, and propose an approach that bypasses this at almost no additional computational cost. Regarding model selection…

二元分类充分降维的主加权支持向量机AI 解读

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its…

结构化相关矩阵建模AI 解读

Ensuring positive definiteness of an estimated structured correlation matrix is challenging. We show that reparameterizing Cholesky factors of correlation matrices using hyperspherical coordinates or angles provides a flexible and effective solution. Once a structured correlation matrix is identified, the corresponding angles and hence the constrained correlations may be estimated by maximum likelihood. Consistency and asymptotic normality of the maximum likelihood…

高维数据中的选择性因子提取AI 解读

This paper studies simultaneous feature selection and extraction in supervised and unsupervised learning. We propose and investigate selective reduced rank regression for constructing optimal explanatory factors from a parsimonious subset of input features. The proposed estimators enjoy sharp oracle inequalities, and with a predictive information criterion for model selection, they adapt to unknown sparsity by controlling both rank and row support of the coefficient matrix.…

环境调查两阶段抽样策略的设计型渐近分析AI 解读

We analyse design-based properties of two-phase strategies for estimating totals and nonlinear functions of totals for environmental populations when the sampling schemes are uniquely determined by points placed in the study region. In the first phase, points are located using tessellation stratified sampling, whereas in the second phase a finite population sampling scheme is adopted. We give sufficient conditions on second-phase designs that ensure consistency, and we…

复杂抽样下多重插补的一则研究AI 解读

Multiple imputation is popular for handling item nonresponse in survey sampling. Current multiple imputation techniques with complex survey data assume that the sampling design is ignorable. In this paper, we propose a new multiple imputation procedure for parametric inference without this assumption. Instead of using the sample-data likelihood, we use the sampling distribution of the pseudo maximum likelihood estimator to derive the posterior distribution of the parameters.…

含测量误差协变量的生存治愈模型：模拟外推推断AI 解读

In many situations in survival analysis, it may happen that a fraction of individuals will never experience the event of interest: they are considered to be cured. The promotion time cure model takes this into account. We consider the case where one or more explanatory variables in the model are subject to measurement error, which should be taken into account to avoid biased estimators. A general approach is the simulation-extrapolation algorithm, a method based on…

柱形 K 函数与泊松直线聚类点过程AI 解读

The analysis of point patterns with linear structures is of interest in many applications. To detect anisotropy in such patterns, in particular in the case of a columnar structure, we introduce a functional summary statistic, the cylindrical |$K$|-function, which is a directional |$K$|-function whose structuring element is a cylinder. We further introduce a class of anisotropic Cox point processes, called Poisson line cluster point processes. The points of such a process are…

存在误分类的混合检测现状数据AI 解读

Group testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of $k$ groups that include $n$ independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all $n$ samples separately (Tu et al., 1994). In some applications,…

存在干扰时的逆概率加权估计量AI 解读

We consider inference about the causal effect of a treatment or exposure in the presence of interference, i.e., when one individual's treatment affects the outcome of another individual. In the observational setting where the treatment assignment mechanism is not known, inverse probability-weighted estimators have been proposed when individuals can be partitioned into groups such that there is no interference between individuals in different groups. Unfortunately this…

使用全局—局部收缩先验的默认贝叶斯分析AI 解读

We provide a framework for assessing the default nature of a prior distribution using the property of regular variation, which we study for global-local shrinkage priors. In particular, we show that the horseshoe priors, originally designed to handle sparsity, are regularly varying and thus are appropriate for default Bayesian analysis. To illustrate our methodology, we discuss four problems of noninformative priors that have been shown to be highly informative for nonlinear…

利用局部线性生存密度进行样本内预测AI 解读

In this paper, in-sample forecasting is defined as forecasting a structured density to sets where it is unobserved. The structured density consists of one-dimensional in-sample components that identify the density on such sets. We focus on the multiplicative density structure, which has recently been seen as the underlying structure of non-life insurance forecasts. In non-life insurance, the in-sample area is defined as one triangle and the forecasting area as the triangle…

结合特征值与特征向量变动确定阶数AI 解读

In applying statistical methods such as principal component analysis, canonical correlation analysis, and sufficient dimension reduction, we need to determine how many eigenvectors of a random matrix are important for estimation. This problem is known as order determination, and amounts to estimating the rank of a matrix. Previous order-determination procedures rely either on the decreasing pattern, or elbow, of the eigenvalues, or on the increasing pattern of the…

准稀疏计数数据的贝叶斯推断AI 解读

There is growing interest in analysing high-dimensional count data, which often exhibit quasi-sparsity corresponding to an overabundance of zeros and small nonzero counts. Existing methods for analysing multivariate count data via Poisson or negative binomial log-linear hierarchical models with zero-inflation cannot flexibly adapt to quasi-sparse settings. We develop a new class of continuous local-global shrinkage priors tailored to quasi-sparse counts. Theoretical…

现代统计理论的若干先驱：个人回顾AI 解读

An outline account is given of the work of nine major figures working mostly in the earlier two-thirds of the 20th century. Some comments are included about their personal characteristics.

时间序列非嵌套模型比较AI 解读

This paper addresses the topic of nonnested time series model comparisons. The main result is a central limit theorem for the likelihood ratio statistic when the models are nonnested and non-equivalent. The concepts of model equivalence and forecast equivalence, which are important for determining the parameter subset corresponding to the null hypothesis, are developed. The method is validated through a simulation study and illustrated on a retail time series.

多元空间协方差模型的条件构造方法AI 解读

Multivariate geostatistics is based on modelling all covariances between all possible combinations of two or more variables at any sets of locations in a continuously indexed domain. Multivariate spatial covariance models need to be built with care, since any covariance matrix that is derived from such a model must be nonnegative-definite. In this article, we develop a conditional approach for spatial-model construction whose validity conditions are easy to check. We start…

利用基线信息进行结局预测的最优分层AI 解读

A common practice in predictive medicine is to use current study data to construct a stratification procedure, which groups subjects according to baseline information and forms stratum-specific prevention or intervention strategies. A desirable stratification scheme would not only have small intra-stratum variation but also have a clinically meaningful discriminatory capability. We show how to obtain optimal stratification rules with such desirable properties from fitting a…

用马尔可夫链片段近似不完整函数数据AI 解读

We consider curve extension and linear prediction for functional data observed only on a part of their domain, in the form of fragments. We suggest an approach based on a combination of Markov chains and nonparametric smoothing techniques, which enables us to extend the observed fragments and construct approximated prediction intervals around them, construct mean and covariance function estimators, and derive a linear predictor. The procedure is illustrated on real and…