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利用分组结构控制错误发现率AI 解读

In the context of large-scale multiple hypothesis testing, the hypotheses often possess certain group structures based on additional information such as Gene Ontology in gene expression data and phenotypes in genome-wide association studies. It is hence desirable to incorporate such information when dealing with multiplicity problems to increase statistical power. In this article, we demonstrate the benefit of considering group structure by presenting a p-value weighting…

纵向数据的函数型变系数模型AI 解读

The proposed functional varying coefficient model provides a versatile and flexible analysis tool for relating longitudinal responses to longitudinal predictors. Specifically, this approach provides a novel representation of varying coefficient functions through suitable auto and cross-covariances of the underlying stochastic processes, which is particularly advantageous for sparse and irregular designs, as often encountered in longitudinal studies. Existing methodology for…

基于个体信息的复检AI 解读

In situations where individuals are screened for an infectious disease or other binary characteristic and where resources for testing are limited, group testing can offer substantial benefits. Group testing, where subjects are tested in groups (pools) initially, has been successfully applied to problems in blood bank screening, public health, drug discovery, genetics, and many other areas. In these applications, often the goal is to identify each individual as positive or…

疾病进展或死亡导致信息性缺失时的治疗因果效应AI 解读

In longitudinal clinical trials, when outcome variables at later time points are only defined for patients who survive to those times, the evaluation of the causal effect of treatment is complicated. In this paper, we describe an approach that can be used to obtain the causal effect of three treatment arms with ordinal outcomes in the presence of death using a principal stratification approach. We introduce a set of flexible assumptions to identify the causal effect and…

部分线性模型的方差分析与 F 检验：环境健康数据应用AI 解读

Fish consumption during pregnancy exposes the fetus to both the neurotoxicant methylmercury and nutrients known to be beneficial for brain development. When nutrient status is not measured, maternal methylmercury levels may be a partial biomarker for both toxic and nutrient exposures. It is therefore necessary to employ a flexible model—such as the partial linear model—that will allow for possible nonlinear trends of methylmercury. To enhance interpretations of fitting a…

用于卫星数据同化的集合卡尔曼滤波与平滑方法AI 解读

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in…

时间序列变点检验AI 解读

This article considers the CUSUM-based (cumulative sum) test for a change point in a time series. In the case of testing for a mean shift, the traditional Kolmogorov–Smirnov test statistic involves a consistent long-run variance estimator, which is needed to make the limiting null distribution free of nuisance parameters. The commonly used lag-window type long-run variance estimator requires to choose a bandwidth parameter and its selection is a difficult task in practice.…

多元随机场的 Matérn 交叉协方差函数AI 解读

We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each constituent component is a Matérn process. The model parameters are interpretable in terms of process variance, smoothness, correlation length, and colocated correlation coefficients, which can be positive or negative. Both the marginal and the cross-covariance functions are of the Matérn type. In a data example on error fields for numerical…

函数型线性模型中的误差相关检验AI 解读

The paper proposes two inferential tests for error correlation in the functional linear model, which complement the available graphical goodness-of-fit checks. To construct them, finite dimensional residuals are computed in two different ways, and then their autocorrelations are suitably defined. From these autocorrelation matrices, two quadratic forms are constructed whose limiting distribution are chi-squared with known numbers of degrees of freedom (different for the two…

分组降维AI 解读

In many regression applications, the predictors fall naturally into a number of groups or domains, and it is often desirable to establish a domain-specific relation between the predictors and the response. In this article, we consider dimension reduction that incorporates such domain knowledge. The proposed method is based on the derivative of the conditional mean, where the differential operator is constrained to the form of a direct sum. This formulation also accommodates…

密度加权平均导数的稳健数据驱动推断AI 解读

This paper presents a novel data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedures…

相关观测线性模型最优设计的新方法AI 解读

We consider the problem of designing experiments for regression in the presence of correlated observations with the location model as the main example. For a fixed correlation structure approximate optimal designs are determined explicitly, and it is demonstrated that under the model assumptions made by Bickel and Herzberg (1979) for the determination of asymptotic optimal design, the designs derived in this article converge weakly to the measures obtained by these authors.…

嵌套格点抽样：随机化嵌套正交表的新抽样方案AI 解读

A nested orthogonal array is an orthogonal array that contains a smaller orthogonal array as a subarray. We construct a new sampling scheme, called nested lattice sampling, by randomizing nested orthogonal arrays with nested permutations. For a pair of nested lattice samples associated with a nested orthogonal array of strength t, the points in both samples achieve uniformity in t or lower dimensions. Some statistical properties of nested lattice samples based on strength…

最小绝对相对误差估计AI 解读

Multiplicative regression model or accelerated failure time model, which becomes linear regression model after logarithmic transformation, is useful in analyzing data with positive responses, such as stock prices or life times, that are particularly common in economic/financial or biomedical studies. Least squares or least absolute deviation are among the most widely used criterions in statistical estimation for linear regression model. However, in many practical…

广义线性潜变量模型的拟合优度检验AI 解读

Generalized Linear Latent Variables Models (GLLVM) enable the modeling of relationships between manifest and latent variables, where the manifest variables are distributed according to a distribution of the exponential family (e.g., binomial or normal) and to the multinomial distribution (for ordinal manifest variables). These models are widely used in social sciences. To test the appropriateness of a particular model, one needs to define a goodness-of-fit test statistic…

有限混合模型的阶数检验AI 解读

The order is an important parameter in applications of finite mixture models. Yet designing a valid and easy-to-use statistical test for the order is challenging. To date, most results on hypothesis tests have focused on homogeneity, a special case where the null model has order 1. In this work, we designed an EM test for the general problem of testing the null hypothesis of order m0 versus an alternative hypothesis of order larger than m0. For any positive integer m0, the…

利用加权核估计方程处理结果缺失的非参数回归AI 解读

We consider nonparametric regression of a scalar outcome on a covariate when the outcome is missing at random (MAR) given the covariate and other observed auxiliary variables. We propose a class of augmented inverse probability weighted (AIPW) kernel estimating equations for nonparametric regression under MAR. We show that AIPW kernel estimators are consistent when the probability that the outcome is observed, that is, the selection probability, is either known by design or…

结构化高维协变量空间中的贝叶斯变量选择及基因组学应用AI 解读

We consider the problem of variable selection in regression modeling in high-dimensional spaces where there is known structure among the covariates. This is an unconventional variable selection problem for two reasons: (1) The dimension of the covariate space is comparable, and often much larger, than the number of subjects in the study, and (2) the covariate space is highly structured, and in some cases it is desirable to incorporate this structural information in to the…

基于新型有序回归模型的老年人功能状态预测AI 解读

The functional mobility of the elderly is a very important factor in aging research, and prognostic information is valuable in making clinical and health care policy decisions. We develop a predictive model for the functional status of the elderly based on data from the Second Longitudinal Study of Aging (LSOA II). The functional status is an ordinal response variable. The ordered probit model has been moderately successful in analyzing such data; however, its reliance on…

利用地区干预强度评估劳动力市场政策AI 解读

We estimate the effects of active labor market policies (ALMPs) on subsequent employment and earnings by nonparametric instrumental variable estimators. Very informative administrative Swiss data with detailed regional information are combined with exogenous regional variation in program participation probabilities to generate an instrument within well-defined local labor markets. We find that implementation of an ALMP increased individual employment probabilities by about…

条件密度估计中的降维与自适应AI 解读

An orthogonal series estimator of the conditional density of a response given a vector of continuous and ordinal/nominal categorical predictors is suggested. The estimator is based on writing a conditional density as a sum of orthogonal projections on all possible subspaces of reduced dimensionality and then estimating each projection via a shrinkage procedure. The shrinkage procedure uses a universal thresholding and a dyadic-blockwise shrinkage for low and high…

利用混合总体证据为超高维聚类选择变量AI 解读

In this paper we develop a nonparametric approach to clustering very high-dimensional data, designed particularly for problems where the mixture nature of a population is expressed through multimodality of its density. Therefore, a technique based implicitly on mode testing can be particularly effective. In principle, several alternative approaches could be used to assess the extent of multimodality, but in the present problem the excess mass method has important advantages.…

调整协变量后的两个有序变量关联检验AI 解读

We propose a new set of test statistics to examine the association between two ordinal categorical variables X and Y after adjusting for continuous and/or categorical covariates Z. Our approach first fits multinomial (e.g., proportional odds) models of X and Y, separately, on Z. For each subject, we then compute the conditional distributions of X and Y given Z. If there is no relationship between X and Y after adjusting for Z, then these conditional distributions will be…

大数据集可数混合模型的后验模拟AI 解读

Mixture models, or convex combinations of a countable number of probability distributions, offer an elegant framework for inference when the population of interest can be subdivided into latent clusters having random characteristics that are heterogeneous between, but homogeneous within, the clusters. Traditionally, the different kinds of mixture models have been motivated and analyzed from very different perspectives, and their common characteristics have not been fully…