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Measurement Error in Linear Autoregressive Models

线性自回归模型中的测量误差

Time series data are often subject to measurement error, usually the result of needing to estimate the variable of interest. Although it is often reasonable to assume that the measurement error is additive (i.e., the estimator is conditionally unbiased for the missing true value), the measurement error variances often vary as a result of changes in the population/process over time and/or changes in sampling effort. In this article we address estimation of the parameters in…

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Population-Calibrated Gene Characterization

基于总体校准的基因表型刻画

Phenotypic characterization of rare disease genes poses a significant statistical challenge, but the need to do so is clear. Clinical management of patients carrying a disease gene depends crucially on an accurate characterization of the genetically predisposed disease, including its likelihood of occurrence among mutation carriers, natural history, and response to treatment. We propose a formal yet practical method for controlling for bias due to ignoring ascertainment,…

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Bootstrapping Unit Root Tests for Autoregressive Time Series

自回归时间序列单位根检验的自助法

The theory developed for bootstrapping unit root tests in an autoregressive (AR) context has been concerned mainly with the large-sample behavior of the methods proposed under the assumption that the null hypothesis is true. No results exist for the relative performance and the power behavior of the bootstrap methods under the alternative. This article studies the properties of different AR bootstrap schemes of the unit root hypothesis, including a new proposal based on…

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Constructing Stationary Time Series Models Using Auxiliary Variables With Applications

利用辅助变量构造平稳时间序列模型及其应用

Here we present a novel method for modeling stationary time series. Our approach is to construct the model with a specified marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation structure. We construct models that parallel existing structures, namely state-space models, autoregressive conditional heteroscedasticity (ARCH) models, and generalized ARCH models. We use Bayesian techniques to…

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Functional Data Analysis for Sparse Longitudinal Data

稀疏纵向数据的函数型数据分析

We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of repeated measurements available per subject is small. In contrast, classical functional data analysis requires a large number of regularly spaced measurements per subject. We assume that the repeated measurements are located randomly with a random number of repetitions for…

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Exact, Nonparametric Inference When Doses Are Measured With Random Errors

剂量含随机测量误差时的精确非参数推断

Studies that estimate the effects of exposure to a possibly harmful agent often compare exposed subjects who received varied doses with matched controls who received zero dose. If the doses are measured with error, then one may wish to use the fallible doses to estimate a linear relationship between the unobserved true dose and the observed response. If one is willing to assume that the dose errors for exposed subjects are symmetrically distributed about 0—that is, the dose…

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Functional Adaptive Model Estimation

函数型自适应模型估计

In this article we are interested in modeling the relationship between a scalar, Y, and a functional predictor, X(t). We introduce a highly flexible approach called functional adaptive model estimation (FAME), which extends generalized linear models (GLMs), generalized additive models (GAMs), and projection pursuit regression (PPR) to handle functional predictors. The FAME approach can model any of the standard exponential family of response distributions that are assumed…

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Estimation of Poisson Intensity in the Presence of Dead Time

存在观测死时间时的泊松强度估计

Phase Doppler interferometry (PDI) is a nonintrusive technique frequently used to obtain information about spray characteristics. Understanding spray characteristics is of critical importance in many areas of science, including liquid fuel spray combustion, spray coatings, fire suppression, and pesticides. PDI measures the size and velocity of individual droplets in a spray. Due to the design of the instrument, recordings of the PDI contain gaps, called dead times. The…

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Diagnostic Checking in ARMA Models With Uncorrelated Errors

误差不相关的 ARMA 模型诊断检验

We consider tests for lack of fit in ARMA models with nonindependent innovations. In this framework, the standard Box–Pierce and Ljung–Box portmanteau tests can perform poorly. Specifically, the usual text book formulas for asymptotic distributions are based on strong assumptions and should not be applied without careful consideration. In this article we derive the asymptotic covariance matrix of a vector of autocorrelations for residuals of ARMA models under weak…

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Sufficient Dimension Reduction via Inverse Regression

通过逆回归实现充分降维

A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimizing a quadratic objective function. An optimal member of this family, the inverse regression estimator (IRE), is proposed, along with inference methods and a computational algorithm. The IRE has at least three desirable properties: (1) Its estimated basis of the central dimension reduction subspace is asymptotically efficient, (2) its test statistic for dimension has an…

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Bayesian Semiparametric Isotonic Regression for Count Data

计数数据的贝叶斯半参数保序回归

This article proposes a semiparametric Bayesian approach for inference on an unknown isotonic regression function, f(x), characterizing the relationship between a continuous predictor, X, and a count response variable, Y, adjusting for covariates, Z. A Dirichlet process mixture of Poisson distributions is used to avoid parametric assumptions on the conditional distribution of Y given X and Z. Then, to also avoid parametric assumptions on f(x), a novel prior formulation is…

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SLEX Analysis of Multivariate Nonstationary Time Series

多元非平稳时间序列的 SLEX 分析

We develop a procedure for analyzing multivariate nonstationary time series using the SLEX library (smooth localized complex exponentials), which is a collection of bases, each basis consisting of waveforms that are orthogonal and time-localized versions of the Fourier complex exponentials. Under the SLEX framework, we build a family of multivariate models that can explicitly characterize the time-varying spectral and coherence properties. Every model has a spectral…

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Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure

暴露缺失时匹配病例对照研究的半参数贝叶斯分析

This article considers Bayesian analysis of matched case-control problems when one of the covariates is partially missing. Within the likelihood context, the standard approach to this problem is to posit a fully parametric model among the controls for the partially missing covariate as a function of the covariates in the model and the variables making up the strata. Sometimes the strata effects are ignored at this stage. Our approach differs not only in that it is Bayesian,…

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Statistical Methods for Eliciting Probability Distributions

概率分布引出的统计方法

Elicitation is a key task for subjectivist Bayesians. Although skeptics hold that elicitation cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subject-matter expert colleagues. This article reviews the state of the art, reflecting the experience of statisticians informed by the fruits of a long line of psychological research into how people represent uncertain information cognitively and how they respond to questions…

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Estimating Size and Composition of Biological Communities by Modeling the Occurrence of Species

通过物种出现模型估计生物群落的规模与组成

We develop a model that uses repeated observations of a biological community to estimate the number and composition of species in the community. Estimators of community-level attributes are constructed from model-based estimators of occurrence of individual species that incorporate imperfect detection of individuals. Data from the North American Breeding Bird Survey are analyzed to illustrate the variety of ecologically important quantities that are easily constructed and…

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Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes

利用分片高斯过程分析非平稳空间数据

In many problems in geostatistics the response variable of interest is strongly related to the underlying geology of the spatial location. In these situations there is often little correlation in the responses found in different rock strata, so the underlying covariance structure shows sharp changes at the boundaries of the rock types. Conventional stationary and nonstationary spatial methods are inappropriate, because they typically assume that the covariance between points…

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An Estimated Likelihood Method for Continuous Outcome Regression Models With Outcome-Dependent Sampling

结果依赖抽样下连续结果回归模型的估计似然方法

Many biomedical observational studies attempt to relate a continuous outcome to an environmental exposure and other important covariates. If the outcome is easier or cheaper to measure relative to the exposure of interest, then the outcome may be observed for every member of a finite-study population, whereas exposure measurements may be obtained only for a relatively small subsample of this population. Rather than selecting a simple random subsample of individuals for…

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A Bayesian Approach to 2000 Census Evaluation Using ACE Survey Data and Demographic Analysis

结合ACE调查与人口分析评估2000年人口普查的贝叶斯方法

Demographic analysis of data on births, deaths, and migration, together with coverage measurement surveys that use capture-recapture methods, have established that U.S. Census counts are flawed for certain subpopulations. Previous work using 1990 Census data in African-Americans age 30–49 proposed a hierarchical Bayesian model that assembled Census, follow-up survey, and demographic data, providing a principled solution to the problem of negative estimated counts in some…

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Phase 2 and 3 Combination Designs to Accelerate Drug Development

加速药物开发的二期与三期联合设计

For late-stage clinical development, we propose combining phase 2 and 3 trials via a two-stage adaptive design. In the first stage, short-term safety and efficacy are examined, after which low doses that lack efficacy and high doses that cause safety concerns are eliminated from further evaluation. The trial continues to the second stage with doses that are not eliminated. For the second stage, the required sample size is adjusted to maintain power. All patients, including…

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Bayesian Variable Selection in Clustering High-Dimensional Data

高维数据聚类中的贝叶斯变量选择

Over the last decade, technological advances have generated an explosion of data with substantially smaller sample size relative to the number of covariates (p ≫ n). A common goal in the analysis of such data involves uncovering the group structure of the observations and identifying the discriminating variables. In this article we propose a methodology for addressing these problems simultaneously. Given a set of variables, we formulate the clustering problem in terms of a…

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Testing Quasi-Independence of Failure and Truncation Times via Conditional Kendall's Tau

基于条件 Kendall τ 的失效时间与截断时间拟独立性检验

Truncated survival data arise when the failure time is observed only if it falls within a subject-specific truncating set. Most analysis methods rely on the key assumption of quasi-independence, that is, factorization of the joint density of failure and truncation times into a product proportional to the individual densities in the observable region. Unlike independence of failure time and censoring time, quasi-independence can be tested. Tests of quasi-independence are…

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