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Domain-Level Covariance Analysis for Multilevel Survey Data With Structured Nonresponse

结构性无应答下多层调查数据的域级协方差分析

Health care quality surveys in the United States are administered to individual respondents (i.e., hospital patients, health plan members) to evaluate the performance of health care organizations (i.e., hospitals, health plans), which thus constitute estimation domains. For better understanding and more parsimonious reporting of dimensions of quality, we analyze relationships among quality measures at the domain level. Rather than specifying a full parametric model for the…

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Does Finasteride Affect the Severity of Prostate Cancer? A Causal Sensitivity Analysis

非那雄胺是否影响前列腺癌严重程度:因果敏感性分析

In 2003 Thompson and colleagues reported that daily use of finasteride reduced the prevalence of prostate cancer by 25% compared to placebo. These results were based on the double-blind randomized Prostate Cancer Prevention Trial (PCPT) which followed 18,882 men with no prior or current indications of prostate cancer annually for seven years. Enthusiasm for the risk reduction afforded by the chemopreventative agent and adoption of its use in clinical practice, however, was…

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Records in Athletics Through Extreme-Value Theory

利用极值理论分析田径世界纪录

We are interested in two questions on extremes relating to world records in athletics. The first question is: What is the ultimate world record in a specific athletic event (such as the 100-m race for men or the high jump for women), given today’s state of the art? Our second question is: How “good” is a current athletic world record? An answer to the second question also enables us to compare the quality of world records in different athletic events. We consider these…

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Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets

大型空间数据集似然估计中的协方差截尾

Maximum likelihood is an attractive method of estimating covariance parameters in spatial models based on Gaussian processes. But calculating the likelihood can be computationally infeasible for large data sets, requiring O(n3) calculations for a data set with n observations. This article proposes the method of covariance tapering to approximate the likelihood in this setting. In this approach, covariance matrixes are “tapered,” or multiplied element wise by a sparse…

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Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects

早期干预效果的多重推断与性别差异:三项实验的重新评估

The view that the returns to educational investments are highest for early childhood interventions is widely held and stems primarily from several influential randomized trials—Abecedarian, Perry, and the Early Training Project—that point to super-normal returns to early interventions. This article presents a de novo analysis of these experiments, focusing on two core issues that have received limited attention in previous analyses: treatment effect heterogeneity by gender…

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Functional Additive Models

函数型加性模型

In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means that the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption and propose to replace it by an additive structure, leading to a more widely applicable and much more flexible framework for functional regression models. The proposed…

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Model-Independent Estimates of Dark Matter Distributions

暗物质分布的模型无关估计

A new nonparametric method is described to estimate the distribution of mass within spherical galaxies. The problem of estimating the mass, M(r), within radius r is converted into a problem of estimating a regression function nonparametrically, subject to shape restrictions. We represent the restrictions by the interception of quadratic cones and use the second-order cone programming to estimate the unknown parameters. We establish asymptotic results that are used to…

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A Directional Model for the Estimation of the Rotation Axes of the Ankle Joint

踝关节旋转轴估计的方向统计模型

This article is motivated by the estimation of the directions of the two rotation axes of the ankle. These axes carry information on individual ankles; they are useful in the construction of biomechanical models and the treatment of orthopedic problems. In biomechanics, the rotation axes of the ankle often are estimated using optimization techniques. This work investigates a statistical model for carrying out the estimation. The data set for analysis is a time-ordered…

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Binary Time Series Modeling With Application to Adhesion Frequency Experiments

二元时间序列建模及其在黏附频率实验中的应用

Repeated adhesion frequency assay is the only published method for measuring the kinetic rates of cell adhesion. Cell adhesion plays an important role in many physiological and pathological processes. Traditional analysis of adhesion frequency experiments assumes that the adhesion test cycles are independent Bernoulli trials. This assumption can often be violated in practice. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating…

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Power-Transformed Linear Quantile Regression With Censored Data

删失数据的幂变换线性分位数回归

We propose a class of power-transformed linear quantile regression models for survival data subject to random censoring. The estimation procedure follows two sequential steps. First, for a given transformation parameter, we can easily obtain the estimates for the regression coefficients by minimizing a well-defined convex objective function. Second, we can estimate the transformation parameter based on a model discrepancy measure by constructing cumulative sum processes. We…

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Variable Inclusion and Shrinkage Algorithms

变量纳入与收缩算法

The Lasso is a popular and computationally efficient procedure for automatically performing both variable selection and coefficient shrinkage on linear regression models. One limitation of the Lasso is that the same tuning parameter is used for both variable selection and shrinkage. As a result, it typically ends up selecting a model with too many variables to prevent overshrinkage of the regression coefficients. We suggest an improved class of methods called variable…

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Partially Linear Additive Hazards Regression With Varying Coefficients

具有变系数的部分线性加性风险回归

Please see the supplementary materials for a correction to this article.To explore the nonlinear interactions between some covariates and an exposure variable, we propose the partially linear additive hazards model for survival data. In a semiparametric setting, we construct a local pseudoscore function to estimate the varying and constant coefficients and establish the asymptotic normality of the proposed estimators. Moreover, we develop the weak convergence property for…

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Optimal Designs for Dose-Finding Studies

剂量探索研究的最优设计

Understanding and properly characterizing the dose–response relationship is a fundamental step in the investigation of a new compound, be it a herbicide or fertilizer, a molecular entity, an environmental toxin, or an industrial chemical. In this article we investigate the problem of deriving efficient designs for the estimation of target doses in the context of clinical dose finding. We propose methods to determine the appropriate number and actual levels of the doses to be…

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Breast Cancer Relative Hazard Estimates From Case–Control and Cohort Designs With Missing Data on Mammographic Density

乳腺密度数据缺失时病例对照与队列设计中的乳腺癌相对风险估计

We analyzed data from the Breast Cancer Detection Demonstration Project (BCDDP) to obtain multivariate relative hazard models for breast cancer that included mammographic density (MD) in addition to standard risk factors. Data from the BCDDP were collected from a stratified case–control study in the screening phase (1973–1980) and from follow-up of three subcohorts in the follow-up phase (1980–1995). For both phases, MD measurements were only available for about half the…

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A Bayesian Capture–Recapture Population Model With Simultaneous Estimation of Heterogeneity

同时估计异质性的贝叶斯捕获再捕获种群模型

We develop a Bayesian capture-recapture model that provides estimates of abundance as well as time-varying and heterogeneous survival and capture probability distributions. The model uses a state-space approach by incorporating an underlying population model and an observation model, and here is applied to photo-identification data to estimate trends in the abundance and survival of a population of bottlenose dolphins (Tursiops truncatus) in northeast Scotland. Novel…

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Prediction of Protein Interdomain Linker Regions by a Nonstationary Hidden Markov Model

利用非平稳隐马尔可夫模型预测蛋白质结构域间连接区

Proteins have two types of regions: linker regions that connect active domains and nonlinker regions, which include domains and terminal residues. It is important to try to distinguish these regions using information in the sequence of amino acids that, when folded, form the protein. In this article we develop a nonstationary hidden Markov model to infer region boundaries. The approach is based on a latent variable defined on the sequence; this variable is a continuous index…

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Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study

前测后测研究中基于经验似然的处理效应估计

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that…

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The Nested Dirichlet Process

嵌套狄利克雷过程

In multicenter studies, subjects in different centers may have different outcome distributions. This article is motivated by the problem of nonparametric modeling of these distributions, borrowing information across centers while also allowing centers to be clustered. Starting with a stick-breaking representation of the Dirichlet process (DP), we replace the random atoms with random probability measures drawn from a DP. This results in a nested DP prior, which can be placed…

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Estimating Equations Inference With Missing Data

缺失数据下的估计方程推断

There is a large and growing body of literature on estimating equation (EE) as an estimation approach. One basic property of EE that has been universally adopted in practice is that of unbiasedness, and there are deep conceptual reasons why unbiasedness is a desirable EE characteristic. This article deals with inference from EEs when data are missing at random. The investigation is motivated by the observation that direct imputation of missing data in EEs generally leads to…

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On Consistent Nonparametric Intensity Estimation for Inhomogeneous Spatial Point Processes

非齐次空间点过程的一致非参数强度估计

A common nonparametric approach to estimate the intensity function of an inhomogeneous spatial point process is through kernel smoothing. When conducting the smoothing, one typically uses events only in a local set around the point of interest. But the resulting estimator often is inconsistent, because the number of events in a fixed set is of order 1 for spatial point processes. In this article we propose a new covariate-based kernel smoothing method to estimate the…

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Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data

高维小样本数据中聚类的统计显著性

Clustering methods provide a powerful tool for the exploratory analysis of high-dimension, low–sample size (HDLSS) data sets, such as gene expression microarray data. A fundamental statistical issue in clustering is which clusters are “really there,” as opposed to being artifacts of the natural sampling variation. We propose SigClust as a simple and natural approach to this fundamental statistical problem. In particular, we define a cluster as data coming from a single…

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On a Projective Resampling Method for Dimension Reduction With Multivariate Responses

多元响应降维的投影重抽样方法

Consider the dimension reduction problem where both the response and the predictor are vectors. Existing estimators of this problem take one of the following routes: (1) targeting the part of the dimension reduction space that is related to the conditional mean (or moments) of the response vector, (2) pooling the estimates for the marginal dimension reduction spaces, and (3) estimating the whole dimension reduction space directly by multivariate slicing. However, the first…

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Assessing Identification Risk in Survey Microdata Using Log-Linear Models

使用对数线性模型评估调查微观数据的再识别风险

This article considers the assessment of the risk of identification of respondents in survey microdata, in the context of applications at the United Kingdom (UK) Office for National Statistics (ONS). The threat comes from the matching of categorical “key“ variables between microdata records and external data sources and from the use of log-linear models to facilitate matching. While the potential use of such statistical models is well established in the literature, little…

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A Test for Partial Differential Expression

部分差异表达的检验

Even in a single-tissue type cancer is often a collection of different diseases, each with its own genetic mechanism. Consequently, a gene may be expressed in some but not all of the tissues in a sample. Differentially expressed genes are commonly detected by methods that test for a shift in location that ignore the possibility of heterogeneous expression. This article proposes a two-sample test statistic designed to detect shifts that occur in only a part of the sample…

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