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Inference on Multivariate M Estimators Based on Bivariate Censored Data

基于二元删失数据的多元 M 估计推断

Consider two random variables subject to random right censoring, for example, the survival times of twins or two recurrence times of a certain disease recorded on the same individual. Of interest is the estimation of a multivariate M functional of these two random variables. Asymptotic results for the proposed estimator are established under general conditions on the criterion function of the M estimator. The obtained results include the asymptotic normality of the…

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Calibration Regression of Censored Lifetime Medical Cost

删失终生医疗费用的校准回归

Analysis of lifetime medical cost is challenged by its irregular distribution and by incomplete follow-up, particularly the latter with limited study duration as is typical in medical research. This article proposes the so-called calibration regression to address these issues by modeling not only lifetime medical cost but also survival time, in a semiparametric fashion. It is postulated that both outcomes, on possibly transformed scales, linearly relate to the covariates;…

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Latent Variable Analysis of Multivariate Spatial Data

多元空间数据的潜变量分析

Multivariate spatial or geo-referenced data arise naturally in such disciplines as ecology, agriculture, geology, and atmospheric sciences. In practice, interest often lies in modeling underlying structure and representing interrelationships in terms of a smaller number of variables. For such situations, statistical analysis using a latent variable model is proposed. We present a general model that incorporates spatial correlation and potential lagged or shifted dependencies…

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Multiple Test Procedures for Identifying the Minimum Effective and Maximum Safe Doses of a Drug

识别药物最低有效剂量与最高安全剂量的多重检验

We address the problem of determining the therapeutic window of a drug by finding its minimum effective and maximum safe doses (MINED and MAXSD). The MINED is the lowest dose that exceeds the mean efficacy of the zero dose by a specified threshold, and the MAXSD is the highest dose that does not exceed the mean toxicity of the zero dose by a specified threshold. Step-down multiple test procedures are proposed to identify the MINED and MAXSD assuming a bivariate normal model.…

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Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models

工具变量模型中分布处理效应的自助法检验

This article considers the problem of assessing the distributional consequences of a treatment on some outcome variable of interest when treatment intake is (possibly) nonrandomized, but there is a binaryinstrument available for the researcher. Such a scenario is common in observational studies and in randomized experiments with imperfect compliance. One possible approach to this problem is to compare the counterfactual cumulative distribution functions of the outcome with…

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Conditional Second-Order Generalized Estimating Equations for Generalized Linear and Nonlinear Mixed-Effects Models

广义线性与非线性混合效应模型的条件二阶广义估计方程

Generalized linear and nonlinear mixed-effects models are used extensively in health care research, including applications in pharmacokinetics, clinical trials, and epidemiology. Because the underlying model may be nonlinear in the random effects, there will generally be no closed-form expression for the marginal likelihood or indeed for the marginal moments. Consequently, estimation is often carried out either using numerical integration techniques or by approximating the…

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Adjusted Nelson–Aalen Estimates With Retrospective Matching

回顾性匹配下调整后的 Nelson–Aalen 估计

In certain situations, randomized trials are not possible, but formal comparison of survival in two or more groups is still desirable. Although no substitute for randomization, application of the matched case-control design to survival studies may minimize the potential bias. Comparable estimates for each treatment group can be obtained by matching or stratifying according to prognostic variables, and weighting each individual to make stratum distributions the same over the…

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Recursive Combination Tests

递归组合检验

We present a method that extends the flexibility of adaptive designs to the number of interim analyses and to the choice of decision boundaries. At each stage of the trial, the design of the next stage can be determined using all of the information gathered so far. Additionally, one can specify the next stage as the final stage or can plan a further interim analysis. The method is based on a recursive application of the two-stage combination tests for p values. The crucial…

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Asymptotic Normality of Semiparametric and Nonparametric Posterior Distributions

半参数与非参数后验分布的渐近正态性

In this article it is shown that the marginal semiparametric and nonparametric posterior distributions for a parameter of interest behave like an ordinary parametric posterior distribution. This in practice provides support of the utility of marginal semiparametric and nonparametric posterior distributions. In particular, the marginal semiparametric and nonparametric posterior distributions are asymptotically normal and centered at the corresponding maximum likelihood…

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Adaptive Model Selection

自适应模型选择

Most model selection procedures use a fixed penalty penalizing an increase in the size of a model. These nonadaptive selection procedures perform well only in one type of situation. For instance, Bayesian information criterion (BIC) with a large penalty performs well for “small” models and poorly for “large” models, and Akaike's information criterion (AIC) does just the opposite. This article proposes an adaptive model selection procedure that uses a data-adaptive complexity…

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Length-Biased Sampling With Right Censoring

伴随右删失的长度偏倚抽样

When survival data arise from prevalent cases ascertained through a cross-sectional study, it is well known that the survivor function corresponding to these data is length biased and different from the survivor function derived from incident cases. Length-biased data have been treated both unconditionally and conditionally in the literature. In the latter case, where length bias is viewed as being induced by random left truncation of the survival times, the truncating…

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Estimation With Survey Data Under Nonignorable Nonresponse or Informative Sampling

不可忽略无应答或信息性抽样下的调查数据估计

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse—that is, a response mechanism that depends on the values of the variable having nonresponse—is the most difficult type of nonresponse to handle. This article studies a likelihood-based estimation method for data with nonignorable nonresponse. The likelihood is semiparametric in the sense that it consists of a parametric component for the response mechanism (such as a logistic model) and a…

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Attributing Effects to Treatment in Matched Observational Studies

An effect is attributable to treatment if it would not have been observed had the individual been exposed to control instead. Extending earlier results on attributable effects in unmatched groups, a method of exact randomization inference and sensitivity analysis is developed for case-referent, case-crossover, and cohort studies with matched sets, and a large sample approximation to the exact inference is given. The unmatched case, considered previously, has certain…

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Nonparametric Estimation of a Distribution Subject to a Stochastic Precedence Constraint

随机先行约束下的分布非参数估计

For any two random variables X and Y with distributions F and G defined on [0,∞), X is said to stochastically precede Y if P(X≤Y) ≥ 1/2. For independent X and Y, stochastic precedence (denoted by X≤spY) is equivalent to E[G(X–)] ≤ 1/2. The applicability of stochastic precedence in various statistical contexts, including reliability modeling, tests for distributional equality versus various alternatives, and the relative performance of comparable tolerance bounds, is…

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Bayesian Smoothing and Regression Splines for Measurement Error Problems

测量误差问题中的贝叶斯平滑样条与回归样条

In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely difficult, the problem being related to deconvolution. Various frequentist approaches exist for this problem, but to date there has been no Bayesian treatment. In this article we describe Bayesian approaches to modeling a flexible regression function when the predictor variable is measured with error. The regression function is modeled with smoothing splines and…

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Inconsistency of Resampling Algorithms for High-Breakdown Regression Estimators and a New Algorithm

高崩溃点回归估计重抽样算法的不一致性与一种新算法

Because high-breakdown estimators (HBEs) are impractical to compute exactly in large samples, approximate algorithms are used. The algorithm generally produces an estimator with a lower consistency rate and breakdown value than the exact theoretical estimator. This discrepancy grows with the sample size, with the implication that huge computations are needed for good approximations in large high-dimension samples. The workhorse for HBEs has been the “elemental set,” or…

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Combining Images Across Multiple Subjects

跨多个受试者整合图像信息

This article introduces a Bayesian hierarchical model for combining information across multiple images. Our work was motivated by an invasive functional brain mapping technique called direct cortical electrical interference that gives a sparse set of binary observations of an underlying “true” region at multiple sites on the brain surface. To model region shapes that may vary widely across individuals, we use mixtures of simple templates, for example, circles. These…

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Bayesian Spatial Prediction of Random Space-Time Fields With Application to Mapping PM 2.5 Exposure

随机时空场的贝叶斯空间预测及 PM2.5 暴露制图应用

This article presents a multivariate spatial prediction methodology in a Bayesian framework. The method is especially suited for use in environmetrics, where vector-valued responses are observed at a small set of ambient monitoring stations “(gauged sites)” at successive time points. However, the stations may have varying start-up times so that the data have a “staircase” pattern (“monotone” pattern in the terminology of Rubin and Shaffer). The lowest step corresponds to the…

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Bayesian Models for Gene Expression With DNA Microarray Data

DNA 微阵列基因表达数据的贝叶斯模型

Two of the critical issues that arise when examining DNA microarray data are (1) determination of which genes best discriminate among the different types of tissue, and (2) characterization of expression patterns in tumor tissues. For (1), there are many genes that characterize DNA expression, and it is of critical importance to try and identify a small set of genes that best discriminate between normal and tumor tissues. For (2), it is critical to be able to characterize…

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Clustering of Translocation Breakpoints

染色体易位断点的聚集分析

Translocation, a physical movement of genetic material from one chromosome to another, can result in the aberrant linkage of two cellular genes. This type of fusion may disrupt cellular function by producing novel, biologically active fused genes, or by triggering the activation of normally quiescent growth-associated genes. Either of these mechanisms provides a putative oncogenic stimulus, and indeed, several gene fusions from translocations have been identified in…

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Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates

含不可忽略退出和缺失协变量的多元纵向结果分析

This article analyzes changes in treatment practices in outpatient methadone treatment units from a national panel study. The analysis of this dataset is challenging due to several difficulties, including multiple longitudinal outcomes, nonignorable nonresponses, and missing covariates. Specifically, the data included several variables that measure the effectiveness of methadone treatment practices for each unit. A substantial percentage of units (33%%) did not respond…

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