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Frailty Survival Model Analysis of the National Deceased Donor Kidney Transplant Dataset Using Poisson Variance Structures

利用泊松方差结构分析亡故供者肾移植数据的脆弱性生存模型

In a recent study of transplant outcomes, donor age, cerebrovascular accident as the cause of death (CVA), renal insufficiency (serum creatinine >1.5 mg/dL), and history of hypertension have been identified as donor factors associated with elevated risk of kidney transplant failure. It is of great interest to know whether there remain other unmeasured donor factors associated with elevated risk of graft failure. In this article we study a sample of 6,024 deceased donor…

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Hierarchical Graphical Models

层级图模型

There is continued debate regarding the exact relation between lower cholesterol levels and increased respiratory disease mortality. One of the goals of this study is to reveal the relationship between subcomponents of cholesterol and pulmonary function. We consider the subcomponents of total cholesterol, namely high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, to investigate the relationship of cholesterol levels with pulmonary function in a…

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Locally Efficient Semiparametric Estimators for Generalized Skew-Elliptical Distributions

广义偏斜椭圆分布的局部有效半参数估计

We consider a class of generalized skew-normal distributions that is useful for selection modeling and robustness analysis and derive a class of semiparametric estimators for the location and scale parameters of the central part of the model. We show that these estimators are consistent and asymptotically normal. We present the semiparametric efficiency bound and derive the locally efficient estimator that achieves this bound if the model for the skewing function is…

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Spike and Slab Gene Selection for Multigroup Microarray Data

多组微阵列数据的尖峰与厚尾基因选择

DNA microarrays can provide insight into genetic changes that characterize different stages of a disease process. Accurate identification of these changes has significant therapeutic and diagnostic implications. Statistical analysis for multistage (multigroup) data is challenging, however. ANOVA-based extensions of two-sample Z-tests, a popular method for detecting differentially expressed genes in two groups, do not work well in multigroup settings. False detection rates…

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Estimation in the Mixture of Markov Chains Moving With Different Speeds

不同移动速度的马尔可夫链混合模型估计

This article considers a new mixture of time-homogeneous finite Markov chains where the mixing is on the rate of movement and develops the EM algorithm for maximum likelihood estimation of the parameters of the mixture. Continuous- and discrete-time versions of the mixture are defined, and their estimation is considered separately. The developed methods are illustrated with an application to modeling bond ratings migration. The class of mixture models proposed in this…

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Estimation of Long Memory in the Presence of a Smooth Nonparametric Trend

存在平滑非参数趋势时的长记忆估计

We consider semiparametric estimation of the long-memory parameter of a stationary process in the presence of an additive nonparametric mean function. We use a semiparametric Whittle-type estimator, applied to the tapered, differenced series. Because the mean function is not necessarily a polynomial of finite order, no amount of differencing will completely remove the mean. We establish a central limit theorem for the estimator of the memory parameter, assuming that a slowly…

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Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing

基于狄利克雷过程混合的贝叶斯非参数空间建模

Customary modeling for continuous point-referenced data assumes a Gaussian process that is often taken to be stationary. When such models are fitted within a Bayesian framework, the unknown parameters of the process are assumed to be random, so a random Gaussian process results. Here we propose a novel spatial Dirichlet process mixture model to produce a random spatial process that is neither Gaussian nor stationary. We first develop a spatial Dirichlet process model for…

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A Two-Way Semilinear Model for Normalization and Analysis of cDNA Microarray Data

用于 cDNA 微阵列归一化与分析的双向半线性模型

A basic question in analyzing cDNA microarray data is normalization, the purpose of which is to remove systematic bias in the observed expression values by establishing a normalization curve across the whole dynamic range. A proper normalization procedure ensures that the normalized intensity ratios provide meaningful measures of relative expression levels. We propose a two-way semilinear model (TW–SLM) for normalization and analysis of microarray data. This method does not…

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Generalized Radius Processes for Elliptically Contoured Distributions

椭圆分布的广义半径过程

The use of Mahalanobis distances has a long history in statistics. Given a sample of size n and general location and scatter estimators, mn and Σn, we can define “generalized” radii as . If we wish to trim observations based on the estimators mn and Σn, then it is natural to first remove the most remote ones (i.e., those with the largest 's). With this in mind, we define a process that maps the trimming proportion, α in (0, 1], to the generalized radius of the observation…

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A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation

物种丰富度估计的惩罚非参数最大似然方法

We propose a class of penalized nonparametric maximum likelihood estimators (NPMLEs) for the species richness problem. We use a penalty term on the likelihood because likelihood estimators that lack it have an extreme instability problem. The estimators are constructed using a conditional likelihood that is simpler than the full likelihood. We show that the full-likelihood NPMLE solution given by Norris and Pollock can be found (with great accuracy) by using an appropriate…

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Dynamical Correlation for Multivariate Longitudinal Data

多元纵向数据的动态相关

Nonparametric methodology for longitudinal data analysis is becoming increasingly popular. The analysis of multivariate longitudinal data, where data on several time courses are recorded for each subject, has received considerably less attention, despite its importance for practical data analysis. In particular, there is a need for measures and estimates to capture dependency between the components of vector-valued longitudinal data. We propose and analyze a simple and…

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Semilinear High-Dimensional Model for Normalization of Microarray Data

微阵列数据归一化的半线性高维模型

Normalization of microarray data is essential for removing experimental biases and revealing meaningful biological results. Motivated by a problem of normalizing microarray data, a semilinear in-slide model (SLIM) has been proposed. To aggregate information from other arrays, SLIM is generalized to account for across-array information, resulting in an even more dynamic semiparametric regression model. This model can be used to normalize microarray data even when there is no…

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The Profile Sampler

剖面似然采样器

We consider frequentist inference for the parametric component θ separately from the nuisance parameter η in semiparametric models based on sampling from the posterior of the profile likelihood. We prove that this procedure gives a first-order–correct approximation to the maximum likelihood estimator and consistent estimation of the efficient Fisher information for θ, without computing derivatives or using complicated numerical approximations. An exact Bayesian…

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Inference for a Class of Transformed Hazards Models

一类变换风险率模型的统计推断

A new class of transformed hazard rate models is considered that contains both the multiplicative hazards model and the additive hazards model as special cases. The sieve maximum likelihood estimators are derived for the model parameters, and the estimators for the regression coefficients are shown to be consistent and asymptotically normal with variance achieving the semiparametric efficiency bound. Simulation studies are conducted to examine the small-sample properties of…

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Rank-Sum Tests for Clustered Data

聚类数据的秩和检验

The Wilcoxon rank-sum test is widely used to test the equality of two populations, because it makes fewer distributional assumptions than parametric procedures such as the t-test. However, the Wilcoxon rank-sum test can be used only if data are independent. When data are clustered, tests based on generalized estimating equations (GEEs) that generalize the t-test have been proposed. Here we develop a rank-sum test that can be used when data are clustered. As an application,…

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Nonparametric Inferences for Additive Models

加性模型的非参数推断

Additive models with backfitting algorithms are popular multivariate nonparametric fitting techniques. However, the inferences of the models have not been very well developed, due partially to the complexity of the backfitting estimators. There are few tools available to answer some important and frequently asked questions, such as whether a specific additive component is significant or admits a certain parametric form. In an attempt to address these issues, we extend the…

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Residual Diagnostics for Growth Mixture Models

增长混合模型的残差诊断

Growth mixture modeling has become a prominent tool for studying the heterogeneity of developmental trajectories within a population. In this article we develop graphical diagnostics to detect misspecification in growth mixture models regarding the number of growth classes, growth trajectory means, and covariance structures. For each model misspecification, we propose a different type of empirical Bayes residual to quantify the departure. Our procedure begins by imputing…

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The Joint Measurement of Technical and Allocative Inefficiencies

技术无效率与配置无效率的联合测度

This article estimates technical and allocative inefficiencies and increase in costs therefrom of individual firms using a translog cost system consisting of the cost function and the cost share equations. We call it a nonlinear random-effects system because technical and allocative inefficiencies are random (hence the term random effects) and are separated from the random noise terms appearing in each equation of the system, and because the inefficiency terms appear in the…

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Multivariate Nonparametric Tests of Independence

多变量独立性的非参数检验

New test statistics are proposed for testing whether two random vectors are independent. Gieser and Randles, as well as Taskinen, Kankainen, and Oja have introduced and discussed multivariate extensions of the quadrant test of Blomqvist. This article serves as a sequel to this work and presents new multivariate extensions of Kendall's tau and Spearman's rho statistics. Two different approaches are discussed. First, interdirection proportions are used to estimate the cosines…

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Semiparametric Regression Analysis of Longitudinal Data With Informative Observation Times

信息性观测时点下纵向数据的半参数回归分析

Statistical analysis of longitudinal data has been discussed by many authors, and a number of methods have been proposed. Most of the research have focused on situations where observation times are independent of or carry no information about the response variable and therefore rely on conditional inference procedures given the observation times. This article considers a different situation, where the independence assumption may not hold; that is, the observation times may…

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A Unified Nonparametric Approach for Unbalanced Factorial Designs

不平衡析因设计的统一非参数方法

Motivated by questions arising from the field of statistical genetics, we consider the problem of testing main, nested, and interaction effects in unbalanced factorial designs. Based on the concept of composite linear rank statistics, a new notion of weighted rank is proposed. Asymptotic normality of weighted linear rank statistics is established under mild conditions, and consistent estimators are developed for the corresponding limiting covariance structure. A unified…

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Bootstrap Standard Error Estimates for Linear Regression

线性回归的自助法标准误估计

Standard errors of parameter estimates are widely used in empirical work. The bootstrap often can provide a convenient means of estimating standard errors. The conditions under which bootstrap standard error estimates are theoretically justified have not received much attention, however. This article establishes conditions for the consistency of the moving blocks bootstrap estimators of the variance of the least squares estimator in linear dynamic models with dependent data.…

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Limited- and Full-Information Estimation and Goodness-of-Fit Testing in 2 n Contingency Tables

高维二元列联表的有限信息与完全信息估计及拟合优度检验

High-dimensional contingency tables tend to be sparse, and standard goodness-of-fit statistics such as X2 cannot be used without pooling categories. As an improvement on arbitrary pooling, for goodness of fit of large 2n contingency tables, we propose classes of quadratic form statistics based on the residuals of margins or multivariate moments up to order r. These classes of test statistics are asymptotically chi-squared distributed under the null hypothesis. Further, the…

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