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Tests of Spatial Randomness Adjusted for an Inhomogeneity

调整空间非均匀性后的空间随机性检验

In many applications, it is of interest to test whether a spatial point pattern is randomly generated after adjusting for an underlying spatial inhomogeneity. A great variety of different test statistics have been proposed for this purpose by scientists in different fields; these are reviewed in this article. Despite apparent dissimilarities in terms of their original formulations, most of these statistics can be placed into one general framework of which they are special…

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Generalized Poststratification and Importance Sampling for Subsampled Markov Chain Monte Carlo Estimation

子抽样 MCMC 估计的广义后分层与重要性抽样

Benchmark estimation is motivated by the goal of producing an approximation to a posterior distribution that is better than the empirical distribution function. This is accomplished by incorporating additional information into the construction of the approximation. We focus here on generalized poststratification, the most successful implementation of benchmark estimation in our experience. We develop generalized poststratification for settings where the source of the…

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On Lower Tolerance Limits With Accurate Coverage Probabilities for the Normal Random Effects Model

正态随机效应模型中覆盖准确的下容忍限

A method for constructing accurate lower tolerance limits for the balanced one-way normal random-effects model is derived by conditioning on an estimator of the unknown expected mean square ratio. Simulation studies indicate that the present procedure is less conservative than that of several existing methods and gives more accurate coverage rates and smaller standard deviations. Numerical examples are given to illustrate the use of the new procedure. Tables needed to…

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Robust M Tests Without Consistent Estimation of the Asymptotic Covariance Matrix

无需一致估计渐近协方差矩阵的稳健 M 检验

We extend the KVB approach of Kiefer, Vogelsang, and Bunzel (2000) to constructing robust M tests without consistent estimation of the asymptotic covariance matrix. We demonstrate that, when model parameters have to be estimated, the normalizing matrix computed using a full-sample estimator is able to eliminate the nuisance parameters when there is no estimation effect but not otherwise. To circumvent the problem of estimation effect, we propose using recursive estimators to…

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Estimation in Multiple-Frame Surveys

多抽样框调查中的估计

Multiple-frame surveys are commonly used to decrease costs of sampling or to reduce undercoverage that could occur if only one sampling frame were used. We describe potential uses and examples of multiple-frame surveys. We then derive optimal linear estimators and pseudo–maximum likelihood estimators for the population total when samples are taken independently from each frame using probability sampling designs. We explore the properties of these estimators theoretically and…

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Functional Variance Processes

函数型方差过程

We introduce the notion of a functional variance process to quantify variation in functional data. The functional data are modeled as samples of smooth random trajectories observed under additive noise. The noise is assumed to be composed of white noise and a smooth random process—the functional variance process—which gives rise to smooth random trajectories of variance. The functional variance process is a tool for analyzing stochastic time trends in noise variance. As a…

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Rank Estimation of Accelerated Lifetime Models With Dependent Censoring

相依删失下加速寿命模型的秩估计

Under independent censoring, estimation of the covariate effects in the accelerated lifetime model may be based on censored data rank tests. Similar rank methodology has been developed with bivariate accelerated lifetime models for dependent censoring but uses artificial censoring, which may lead to substantial information loss. We present a new artificial censoring technique using pairwise ranking and establish the asymptotic properties of a pairwise rank estimator.…

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Estimating a Unimodal Distribution From Interval-Censored Data

从区间删失数据估计单峰分布

In this article we consider three nonparametric maximum likelihood estimators based on mixed-case interval-censored data. Apart from the unrestricted estimator, we consider estimators under the assumption that the underlying distribution function of event times is concave or unimodal. Characterizations of the estimates are derived, and algorithms are proposed for their computation. The estimators are shown to be asymptotically consistent, and the benefits of additional…

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On m out of n Bootstrapping for Nonstandard M-Estimation With Nuisance Parameters

含干扰参数的非标准 M 估计之 m-out-of-n 自助法

Nonstandard M-estimation, with nuisance parameters consistently estimated in the criterion function, often yields M-estimators converging weakly at rates different from n1/2 with weak limits that are typically non-Gaussian. The complicated asymptotics involved makes distributional estimation of the M-estimators analytically prohibitive. We show that the problem is resolved by m out of n bootstrapping under very general conditions, which provides a universal and convenient…

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Doubly Robust Estimation of the Area Under the Receiver-Operating Characteristic Curve in the Presence of Verification Bias

存在确诊选择偏差时 ROC 曲线下面积的双重稳健估计

The area under the receiver operating characteristic curve (AUC) is a popular summary measure of the efficacy of a medical diagnostic test to discriminate between healthy and diseased subjects. A frequently encountered problem in studies that evaluate a new diagnostic test is that not all patients undergo disease verification because the verification test is expensive, invasive, or both. Furthermore, the decision to send patients to verification often depends on the new test…

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Randomization Inference With Natural Experiments

自然实验中的随机化推断

Since the 2000 U.S. Presidential election, social scientists have rediscovered a long tradition of research examining the effects of ballot format on voting. Using a new dataset collected by The New York Times, we investigate the causal effect of being listed on the first ballot page in the 2003 California gubernatorial recall election. California law mandates a unique randomization procedure of ballot order that, when appropriately modeled, can be used to approximate a…

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Quantile Autoregression

分位数自回归

We consider quantile autoregression (QAR) models in which the autoregressive coefficients can be expressed as monotone functions of a single, scalar random variable. The models can capture systematic influences of conditioning variables on the location, scale, and shape of the conditional distribution of the response, and thus constitute a significant extension of classical constant coefficient linear time series models in which the effect of conditioning is confined to a…

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Evaluating Kindergarten Retention Policy

幼儿园留级政策的评估

This article considers the policy of retaining low-achieving children in kindergarten rather than promoting them to first grade. Under the stable unit treatment value assumption (SUTVA) as articulated by Rubin, each child at risk of retention has two potential outcomes: Y(1) if retained and Y(0) if promoted. But SUTVA is questionable, because a child's potential outcomes will plausibly depend on which school that child attends and also on treatment assignments of other…

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Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration

用边际积分估计和检验加性模型中的变系数

We propose marginal integration estimation and testing methods for the coefficients of varying-coefficient multivariate regression models. Asymptotic distribution theory is developed for the estimation method, which enjoys the same rate of convergence as univariate function estimation. For the test statistic, asymptotic normal theory is established. These theoretical results are derived under the fairly general conditions of absolute regularity (β-mixing). Application of the…

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Nonparametric Two-Sample Methods for Ranked-Set Sample Data

排序集抽样数据的非参数两样本方法

A new collection of procedures is developed for the analysis of two-sample, ranked-set samples, providing an alternative to the Bohn–Wolfe procedure. These procedures split the data based on the ranks in the ranked-set sample and lead to tests for the centers of distributions, confidence intervals, and point estimators. The advantages of the new tests are that they require essentially no assumptions about the mechanism by which rankings are imperfect, that they maintain…

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Likelihood Subgradient Densities

似然次梯度密度

We introduce likelihood subgradient densities and explore their basic properties. Using mixtures of likelihood subgradient densities, we propose an approach for constructing tight enveloping functions in the Bayesian context. In the case of normal priors with normal data, the area underneath the resulting enveloping function is bounded above by . The approach is extended to k-dimensional models where the corresponding bound is . More generally, our approach should also yield…

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Geoadditive Survival Models

地理加性生存模型

Survival data often contain small-area geographical or spatial information, such as the residence of individuals. In many cases, the impact of such spatial effects on hazard rates is of considerable substantive interest. Therefore, extensions of known survival or hazard rate models to spatial models have been suggested. Mostly, a spatial component is added to the usual linear predictor of the Cox model. In this article flexible continuous-time geoadditive models are…

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Efficient Estimation of Semiparametric Multivariate Copula Models

半参数多元 Copula 模型的有效估计

We propose a sieve maximum likelihood estimation procedure for a broad class of semiparametric multivariate distributions. A joint distribution in this class is characterized by a parametric copula function evaluated at nonparametric marginal distributions. This class of distributions has gained popularity in diverse fields due to its flexibility in separately modeling the dependence structure and the marginal behaviors of a multivariate random variable, and its…

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Statistical Inference for the Difference Between the Best Treatment Mean and a Control Mean

最佳处理均值与对照均值之差的统计推断

In many experiments, researchers are interested in comparing several treatment means with a control mean. When there are some treatments significantly better than the control, it is often of interest to evaluate the difference between the best treatment mean and the control mean and to identify the best treatment. In this article we derive lower confidence bounds for the aforementioned difference for the case that treatments are at least as effective as the control and for…

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Locally Efficient Estimation With Bivariate Right-Censored Data

二元右删失数据的局部有效估计

Estimation of the survival curve for independently right-censored bivariate failure time data is a problem that has been studied extensively over the past 20 years. In this article we propose a new class of estimators for the bivariate survivor function based on locally efficient (LE) estimation. The LE estimator takes bivariate estimators Fn and Gn of the distributions of the time variables (T1, T2) and the censoring variables (C1, C2), and maps them to the resulting…

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Bayes Linear Calibrated Prediction for Complex Systems

复杂系统的贝叶斯线性校准预测

A calibration-based approach is developed for predicting the behavior of a physical system that is modeled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at parameterizations that appear to give good matches to those observations. This approach can be applied to complex high-dimensional systems with expensive simulators, where a fully Bayesian approach would be impractical. It is…

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Testing for Racial Profiling in Traffic Stops From Behind a Veil of Darkness

利用黑暗遮蔽检验交通拦截中的种族差别对待

The key problem in testing for racial profiling in traffic stops is estimating the risk set, or “benchmark,” against which to compare the race distribution of stopped drivers. To date, the two most common approaches have been to use residential population data or to conduct traffic surveys in which observers tally the race distribution of drivers at a certain location. It is widely recognized that residential population data provide poor estimates of the population at risk…

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Principal Components Analysis Based on Multivariate MM Estimators With Fast and Robust Bootstrap

基于多元 MM 估计及快速稳健自助法的主成分分析

We consider robust principal components analysis (PCA) based on multivariate MM estimators. We first study the robustness and efficiency of these estimators, particularly in terms of eigenvalues and eigenvectors. We then focus on inference procedures based on a fast and robust bootstrap for MM estimators. This method is an alternative to the approach based on the asymptotic distribution of the estimators and can also be used to assess the stability of the principal…

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