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A Marginal Model for Analyzing Discrete Outcomes From Longitudinal Surveys With Outcomes Subject to Multiple-Cause Nonresponse

多原因无应答下纵向调查离散结局分析的边缘模型

Techniques for analyzing categorical outcomes obtained from longitudinal survey samples, with outcomes subject to multiple-cause nonresponse, are developed within the framework of weighted generalized estimating equations. Development of these techniques was motivated by disability data obtained from the Longitudinal Study of Aging (LSOA), a longitudinal survey sample containing missing follow-up for many elderly participants. We posit a model that combines different…

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Multiple Test Procedures for Identifying the Maximum Safe Dose

识别最大安全剂量的多重检验程序

We consider dose response studies for safety assessment of crop protection compounds and drugs, and we offer a hypothesis testing approach for identifying the maximum dose level that is guaranteed to be safe with preassigned confidence. The focus is on step-down (SD) multiple test procedures for identifying the maximum safe dose. We propose two classes of contrasts among the dose means as test statistics for these procedures: pairwise contrasts (PC) and Helmert contrasts…

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Parametric and Nonparametric Methods for Understanding the Relationship Between Carcinogen-Induced DNA Adduct Levels in Distal and Proximal Regions of the Colon

结肠远端与近端致癌物诱导 DNA 加合物水平关系的参数与非参数方法

An important problem in studying the etiology of colon cancer is understanding the relationship between DNA adduct levels (broadly, DNA damage) in cells within colonic crypts in distal and proximal parts of the colon, following treatment with a carcinogen and different types of diet. In particular, it is important to understand whether rats who have elevated adduct levels in particular positions in distal region crypts also have elevated levels in the same positions of the…

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Simple Robust Testing of Hypotheses in Nonlinear Models

非线性模型中的简便稳健假设检验

We develop test statistics to test hypotheses in nonlinear weighted regression models with serial correlation or conditional heteroscedasticity of unknown form. The novel aspect is that these tests are simple and do not require the use of heteroscedasticity autocorrelationconsistent (HAC) covariance matrix estimators. This new class of tests uses stochastic transformations to eliminate nuisance parameters as a substitute for consistently estimating the nuisance parameters.…

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Analyzing Recurrent Event Data With Informative Censoring

信息性删失下的重复事件数据分析

Recurrent event data are frequently encountered in longitudinal follow-up studies. In statistical literature, noninformative censoring is typically assumed when statistical methods and theory are developed for analyzing recurrent event data. In many applications, however, the observation of recurrent events could be terminated by informative dropouts or failure events, and it is unrealistic to assume that the censoring mechanism is independent of the recurrent event process.…

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Robust Inference for Generalized Linear Models

广义线性模型的稳健推断

By starting from a natural class of robust estimators for generalized linear models based on the notion of quasi-likelihood, we define robust deviances that can be used for stepwise model selection as in the classical framework. We derive the asymptotic distribution of tests based on robust deviances, and we investigate the stability of their asymptotic level under contamination. The binomial and Poisson models are treated in detail. Two applications to real data and a…

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Downweighting Influential Clusters in Surveys

调查中高影响群组的降权处理

Certain clusters may be extremely influential on survey estimates and consequently contribute disproportionately to their variance. We propose a general approach to estimation that downweights highly influential clusters, with the amount of downweighting based on M-estimation applied to the empirical influence of the clusters. The method is motivated by a problem in census coverage estimation, and we illustrate it by using data from the 1990 Post Enumeration Survey (PES). In…

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Statistical Analysis of Ion Channel Data Using Hidden Markov Models With Correlated State-Dependent Noise and Filtering

含相关状态依赖噪声与滤波效应的离子通道隐马尔可夫分析

A hidden Markov model that describes ion channel data, including correlated, state-dependent noise and filter characteristics, and a Markov chain Monte Carlo algorithm that enables Bayesian inference under this model are presented. The method provides parameter estimates and an estimate of the noiseless signal. It was tested on simulated data and applied to real recordings to estimate the model parameters. Modeling the noise and filter correctly turned out to be crucial for…

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Estimation of the Head Sensitivity Function in Scanning Magnetoresistance Microscopy

扫描磁阻显微术中磁头灵敏度函数的估计

We apply Bayesian image analysis techniques to a problem in a newly developed scanned probe technology that uses commercial magnetoresistive (MR) record and playback heads as probes to sense magnetic fields. This technology can be used for magnetic imaging and for evaluating playback and record processes in magnetic recording. In MR microscopy, an MR head is raster scanned while in physical contact with a magnetic sample (e.g., hard disk media, tape, or fine magnetic…

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A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data

半连续纵向数据的两部分随机效应模型

A semicontinuous variable has a portion of responses equal to a single value (typically 0) and a continuous, often skewed, distribution among the remaining values. In cross-sectional analyses, variables of this type may be described by a pair of regression models; for example, a logistic model for the probability of nonzero response and a conditional linear model for the mean response given that it is nonzero. We extend this two-part regression approach to longitudinal…

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Linear Transformation Models for Failure Time Data With Covariate Measurement Error

含协变量测量误差的失效时间线性变换模型

In medical studies, patients' biological parameters are often imprecisely measured due to the measuring mechanism or the biological variability. In the presence of covariate measurement error, survival analysis using the Cox model with the observed covariate may yield a biased estimate for the regression parameter. Existing research on this topic has focused on adapting the Cox model to covariates with measurement errors. In this article we generalize linear transformation…

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James-Stein-Type Estimators in Large Samples With Application to the Least Absolute Deviations Estimator

We explore the extension of James–Stein–type estimators in a direction that enables them to preserve their superiority when the sample size goes to infinity. Instead of shrinking a base estimator toward a fixed point, we shrink it toward a data-dependent point. We provide an analytic expression for the asymptotic risk and bias of James–Stein–type estimators shrunk toward a data-dependent point and prove that they have smaller asymptotic risk than the base estimator.…

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Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models

实参数进化蒙特卡洛及其在贝叶斯混合模型中的应用

We propose an evolutionary Monte Carlo algorithm to sample from a target distribution with real-valued parameters. The attractive features of the algorithm include the ability to learn from the samples obtained in previous steps and the ability to improve the mixing of a system by sampling along a temperature ladder. The effectiveness of the algorithm is examined through three multimodal examples and Bayesian neural networks. The numerical results confirm that the real-coded…

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Goodness-of-Fit Tests for Parametric Regression Models

参数回归模型的拟合优度检验

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric fits is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates of contiguous alternatives that can be detected consistently by the adaptive…

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Empirical Bayes Approach to Improve Wavelet Thresholding for Image Noise Reduction

用于改善图像降噪小波阈值处理的经验贝叶斯方法

Wavelet threshold algorithms replace small magnitude wavelet coefficients with zero and keep or shrink the other coefficients. This is basically a local procedure, because wavelet coefficients characterize the local regularity of a function. Although a wavelet transform has decorrelating properties, structures in images, like edges, are never decorrelated completely, and these structures appear in the wavelet coefficients: a classification based on a local criterion-like…

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A Theory for Dynamic Weighting in Monte Carlo Computation

蒙特卡洛计算中动态加权的理论

This article provides a first theoretical analysis of a new Monte Carlo approach, the dynamic weighting algorithm, proposed recently by Wong and Liang. In dynamic weighting Monte Carlo, one augments the original state space of interest by a weighting factor, which allows the resulting Markov chain to move more freely and to escape from local modes. It uses a new invariance principle to guide the construction of transition rules. We analyze the behavior of the weights…

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Automatic Statistical Analysis of Bivariate Nonstationary Time Series

二元非平稳时间序列的自动统计分析

We propose a new method for analyzing bivariate nonstationary time series. The proposed method is a statistical procedure that automatically segments the time series into approximately stationary blocks and selects the span to be used to obtain the smoothed estimates of the time-varying spectra and coherence. It is based on the smooth localized complex exponential (SLEX) transform, which forms a library of orthogonal complex-valued transforms that are simultaneously…

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Pay Phones, Parking Meters, Vending Machines, and Optimal Bayesian Decisions on Collection Times

公用电话、停车收费器与自动售货机的最优贝叶斯收款时点

Payphones, parking meters, and vending machines illustrate the modern business practice of substituting machinery for manpower. They do not eliminate manual labor completely, because full coin-boxes still must be replaced and vending machines still must be stocked. Deciding when to replace a coin-box is important, with unequal losses resulting from underestimation and overestimation. This article derives optimal methodology for this problem by incorporating collection…

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