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Estimation in Dual Frame Surveys with Complex Designs

复杂抽样设计下双抽样框调查的估计

In a dual frame survey, samples are drawn independently from two overlapping frames that are assumed to cover the population of interest. This article considers the case when at least one of the samples is selected by a complex design involving, e.g., multistage sampling. A “pseudo”—maximum likelihood estimator of a population total or a mean for such dual frame surveys is proposed. An advantage of the proposed estimator is that the same weights are used for all the…

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On Balanced Half-Sample Variance Estimation in Stratified Random Sampling

分层随机抽样中的平衡半样本方差估计

Establishment surveys based on list frames often use stratified random sampling with a small number of strata, H, and relatively large sample sizes, n h , within strata. For such surveys, a grouped balanced half-sample (GBHS) method is often used for variance estimation and for construction of confidence intervals on population parameters of interest. In this method the sample in each stratum is first randomly divided into two groups, and then the balanced half-sample (BHS)…

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Nonequivariant Simultaneous Confidence Intervals Less Likely to Contain Zero

更不易包含零的非等变同时置信区间

We present a procedure for finding simultaneous confidence intervals for the expectations μ = (μ j ) n j=1 of a set of independent random variables, identically distributed up to their location parameters, that yields intervals less likely to contain zero than the standard simultaneous confidence intervals for many μ ≠ 0. The procedure is defined implicitly by inverting a nonequivariant hypothesis test with a hyperrectangular acceptance region whose orientation depends on…

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Estimation of the Generalized Prediction Error Variance of a Multiple Time Series

多元时间序列广义预测误差方差的估计

For a multivariate stationary time series, we propose a nonparametric estimator for its generalized prediction error variance using a multivariate analog of the Szegö-Kolmogorov formula, replacing the integral by a sum and replacing the unknown spectral density matrix by a consistent estimator. Asymptotic normality of this estimator is established, and its small-sample behavior is assessed through simulation and application to two real data sets. These examples show that the…

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Spline Estimation of Paths Using Bearings-Only Tracking Data

利用仅含方位角的跟踪数据进行路径样条估计

In many applications, bearings are measured to a moving object with the goal of estimating the object's course of movement. If movement is appropriately modeled as a smooth deterministic curve in the plane, then a cubic spline is a reasonable representation of the curve. Maximum likelihood estimators are presented for parameters of regression splines, assuming that observation errors follow a Von Mises distribution. Location estimates are obtainable even when data are…

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Finite-Sample Variance of Local Polynomials: Analysis and Solutions

局部多项式的有限样本方差:分析与解决方法

Fitting local polynomials in nonparametric regression has a number of advantages. The attractive theoretical features are in a partial contradiction to variance properties for random design and to practical experience over a broad range of situations. No upper bound can be given for the conditional variance. The unconditional variance is infinite when using optimal weights with compact support. Properties are better for Gaussian weights. We analyze local polynomials for…

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On Local Smoothing of Nonparametric Curve Estimators

非参数曲线估计的局部平滑

We develop new local versions of familiar smoothing methods, such as cross-validation and smoothed cross-validation, in the contexts of density estimation and regression. These new methods are locally adaptive in the sense that they capture smooth local fluctuations in the curve by using smoothly varying bandwidths that change as the character of the curve changes. Moreover, the new methods are accurate, easy to apply, and computationally expedient.

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Asymptotics for the SIMEX Estimator in Nonlinear Measurement Error Models

非线性测量误差模型中 SIMEX 估计量的渐近理论

Cook and Stefanski have described a computer-intensive method, the SIMEX method, for approximately consistent estimation in regression problems with additive measurement error. In this article we derive the asymptotic distribution of their estimators and show how to compute estimated standard errors. These standard error estimators can either be used alone or as prepivoting devices in a bootstrap analysis. We also give theoretical justification to some of the phenomena…

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Bootstrap Confidence Region Estimation of the Motion of Rigid Bodies

刚体运动的 bootstrap 置信域估计

This article deals with statistical inference of the motion of rigid bodies using bootstrap methodology. We consider two types of motion: motion in p dimensional Euclidean space, and motion on a p-dimensional sphere. This article is motivated by problems of interest to polar scientists understanding the motion of ice pack at the North Pole and those to geoscientists studying the motion of tectonic plates. In addition to obtaining the point estimates for various parameters…

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A Path Length Inequality for the Multivariate- t Distribution, with Applications to Multiple Comparisons

多元 t 分布的路径长度不等式及其多重比较应用

This article presents a new inequality for the multivariate-t distribution, which implies a new method for multiple comparisons whose foundation rests on a recent inequality due to Naiman. The new method is promising in view of the fact that it utilizes information (estimator intercorrelations) ignored by the most widely used multiple comparison methods yet is not computationally prohibitive, requiring only the numerical evaluation of a single one-dimensional integral. In…

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The Matrix-Logarithmic Covariance Model

矩阵对数协方差模型

A flexible method is introduced to model the structure of a covariance matrix C and study the dependence of the covariances on explanatory variables by observing that for any real symmetric matrix A, the matrix exponential transformation, C = exp (A), is a positive definite matrix. Because there is no constraint on the possible values of the upper triangular elements on A, any possible structure of interest can be imposed on them. The method presented here is not intended to…

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Bayesian Experimental Design for Multiple Hypothesis Testing

面向多重假设检验的贝叶斯实验设计

The problem of designing an optimal experiment for the purpose of performing one or more hypothesis tests is considered. The Bayesian decision theoretic approach is used to arrive at several new optimality criteria for this purpose. For a single hypothesis test, the resulting optimal designs are the well-known φ-optimal designs that minimize the posterior variance of the parameter being tested. For multiple tests, an experimental design must perform well under several…

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Studying Convergence of Markov Chain Monte Carlo Algorithms Using Coupled Sample Paths

利用耦合样本路径研究 MCMC 算法的收敛

I describe a simple procedure for investigating the convergence properties of Markov chain Monte Carlo sampling schemes. The procedure uses coupled chains from the same sampler, obtained by using the same sequence of random deviates for each run. By examining the distribution of the iteration at which all sample paths couple, convergence properties for the system can be established. The procedure also provides a simple diagnostic for detecting modes in multimodal posteriors.…

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Bayesian Inference for Semiparametric Binary Regression

半参数二元回归的贝叶斯推断

We propose a regression model for binary response data that places no structural restrictions on the link function except monotonicity and known location and scale. Predictors enter linearly. We demonstrate Bayesian inference calculations in this model. By modifying the Dirichlet process, we obtain a natural prior measure over this semiparametric model, and we use Polya sequence theory to formulate this measure in terms of a finite number of unobserved variables. We design a…

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Likelihood Inference for Permuted Data with Application to Gene Regulation

置换数据的似然推断及其基因调控应用

Given that all the cells of an individual have the same genetic information stored in their DNA, how can cells be as different as those of the retina and heart? Nature solves this problem through gene regulation, which often involves the binding of regulatory proteins to regulatory sites. These sites are short subsequences of 10 to 20 DNA base pairs whose pattern may be multinomially modeled. These sites usually occur “upstream” of the genes they regulate in a segment of a…

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Nonparametric Mixed-Effects Models for Repeated Binary Data Arising in Serial Dilution Assays: An Application to Estimating Viral Burden in AIDS

连续稀释试验重复二元数据的非参数混合效应模型:艾滋病病毒负荷估计

This article develops methods for estimating treatment effects in mixed-effects models using outcome data gathered from serial dilution assays. Our application allows us to estimate the viral burden of HIV infection before and after antiviral treatment from cell dilution assays. This assay is designed to determine the infectious units per patient peripheral blood mononuclear cell (PBMC). The infectious unit is the amount of virus required to produce detectable HIV infection…

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On Bayesian Analysis of Multirater Ordinal Data: An Application to Automated Essay Grading

多评分者有序数据的贝叶斯分析:自动作文评分应用

A framework is proposed for the analysis of ordinal categorical data when ratings from several judges are available. I emphasize the tasks of estimating latent trait characteristics of individual items, regressing these latent traits on observed covariates, and comparing the performance of raters. The model is illustrated in the design and evaluation of an automated essay grader. This grader is based on a regression of variables, obtained from a grammar checker, on essay…

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A Brief Survey of Bandwidth Selection for Density Estimation

密度估计带宽选择简述

There has been major progress in recent years in data-based bandwidth selection for kernel density estimation. Some “second generation” methods, including plug-in and smoothed bootstrap techniques, have been developed that are far superior to well-known “first generation” methods, such as rules of thumb, least squares cross-validation, and biased cross-validation. We recommend a “solve-the-equation” plug-in bandwidth selector as being most reliable in terms of overall…

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A Review of the Development and Application of Recursive Residuals in Linear Models

线性模型中递归残差的发展与应用综述

Recursive residuals have been shown to be useful in a variety of applications in linear models. Unlike the more familiar ordinary least squares residuals or studentized residuals, recursive residuals are independent as well as homoscedastic under the model. Their independence is particularly appealing for use in developing test statistics. They are not uniquely defined; their values depend on the order in which they are calculated, although their properties do not. In some…

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Optimal Stopping with Random Horizon with Application to the Full-Information Best-Choice Problem with Random Freeze

随机期限下的最优停止及其在随机冻结的完全信息最优选择问题中的应用

A general theorem is given showing that an optimal stopping problem with independent random horizon is essentially equivalent to a “discounted” fixed horizon optimal stopping problem derived from it. The result is used to treat the “full information” secretary problem, where a known number of applicants for a job are potentially available. Applicants are interviewed sequentially with no recall, and their X i -values are observed, where the X i are iid random variables from a…

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Lower Confidence Bounds Using Pilot Samples with an Application to Auditing

利用先导样本构造置信下界及其审计应用

A pilot study is often done prior to a more thorough study, to determine whether a larger-scale effort is likely to yield useful results. In one sense, the pilot study can discourage or encourage further investigation. This is true in many situations in auditing. For example, an auditor seeks to determine that amount an insurance provider overpays. Often the provider makes few mistakes and overpays little. On the other hand, occasionally a provider overpays a lot. Because…

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Estimation of Rate and Mean Functions from Truncated Recurrent Event Data

截断重复事件数据的发生率与均值函数估计

This article investigates data on recurrent events that arise from sources such as warranty claims, where the observation period for a unit is unknown until it experiences at least one event. This creates a type of truncation in the data. We consider nonparametric estimation of means and rates of the event occurrences with such “zero-truncated” data and examine the case where the population size and the distribution of observation times across units are at least…

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Discrimination of Time Series by Parametric Filtering

通过参数滤波区分时间序列

A new approach to time series discrimination is discussed. The approach, called parametric filtering, combines a parametric filter bank with an analysis of first-order autocorrelation from the filtered time series. As a function of the filter parameter, the first-order autocorrelation is shown to uniquely characterize the correlation structure of the original stationary time series. It is also shown to produce a diagnostic that not only characterizes correlation structures,…

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