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利用卷积模型对质子加速器进行统计推断AI 解读

Proton beams present difficulties in analysis because of the limited data that can be collected. The study of such beams must depend on complex computer simulators that incorporate detailed physical equations. The statistical problem of interest is to infer the initial state of the beam from the limited data collected as the beam passes through a series of focusing magnets. We are thus faced with a classic inverse problem where the computer simulator links the initial state…

包含治疗选择的混合干预试验中的因果推断AI 解读

Randomized allocation of treatments is a cornerstone of experimental design but has drawbacks when a limited set of individuals are willing to be randomized, or the act of randomization undermines the success of the treatment. Choice-based experimental designs allow a subset of the participants to choose their treatments. We discuss here causal inferences for hybrid experimental designs in which some participants are randomly allocated to treatments and others receive their…

任职优势及其差异的估计：一项前后比较研究AI 解读

Incumbency advantage is one of the most widely studied features in American legislative elections. In this article we construct and implement an estimate that allows incumbency advantage to vary between individual incumbents. This model predicts that open-seat elections will be less variable than those with incumbents running, an observed empirical pattern that is not explained by previous models. We apply our method to the U.S. House of Representatives in the twentieth…

统计辅助的处理方案选择AI 解读

Choosing among a number of treatments the most suitable for a particular client is an issue of everyday concern. In this article two methodological advances for statistically assisted treatment choice are developed: First, it permits one to combine a dataset on previously treated clients with a dataset on new clients when the regressors available in these two datasets do not coincide. It thereby incorporates additional regressors on previously treated clients that are not…

功能相关基因中多态性的贝叶斯选择与聚类AI 解读

In epidemiologic studies, there is often interest in assessing the relationship between polymorphisms in functionally related genes and a health outcome. For each candidate gene, single nucleotide polymorphism (SNP) data are collected at a number of locations, resulting in a large number of possible genotypes. Because instabilities can result in analyses that include all the SNPs, dimensionality is typically reduced by conducting single SNP analyses or attempting to identify…

装袋法对经济时间序列预测有多大帮助？美国消费价格通胀案例AI 解读

This article focuses on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction mean squared error of forecasting models of U.S. consumer price inflation. We propose three variants of the bagging algorithm specifically designed for this type of forecasting problem and evaluate their empirical performance. Although bagging predictors in our application are clearly more accurate than equally weighted forecasts, median…

半参数过度离散广义线性模型中的变量选择与模型平均AI 解读

We express the mean and variance terms in a double-exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model and whether they enter linearly or flexibly. When the variance term is null, we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation, and the…

存在个体间干扰时的因果推断AI 解读

A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this…

用于时间序列分析的拉普拉斯周期图AI 解读

A new type of periodogram, called the Laplace periodogram, is derived by replacing least squares with least absolute deviations in the harmonic regression procedure that produces the ordinary periodogram of a time series. An asymptotic analysis reveals a connection between the Laplace periodogram and the zero-crossing spectrum. This relationship provides a theoretical justification for use of the Laplace periodogram as a nonparametric tool for analyzing the serial dependence…

半参数回归模型中的惩罚估计函数与变量选择AI 解读

We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the…

利用 EM 类算法估计地震学中的时空分枝过程模型AI 解读

Maximum likelihood estimation of branching point process models via numerical optimization procedures can be unstable and computationally intensive. We explore an alternative estimation method based on the expectation-maximization algorithm. The method involves viewing the estimation of such branching processes as analogous to incomplete data problems. Using an application from seismology, we show how the epidemic-type aftershock sequence (ETAS) model can, in fact, be…

利用高维输出校准计算机模型AI 解读

This work focuses on combining observations from field experiments with detailed computer simulations of a physical process to carry out statistical inference. Of particular interest here is determining uncertainty in resulting predictions. This typically involves calibration of parameters in the computer simulator as well as accounting for inadequate physics in the simulator. The problem is complicated by the fact that simulation code is sufficiently demanding that only a…

非线性模型中的测量误差：现代视角AI 解读

(2008). Measurement Error in Nonlinear Models: A Modern Perspective. Journal of the American Statistical Association: Vol. 103, No. 481, pp. 427-427.

摘要暂缺

统计推断要义AI 解读

(2008). Essentials of Statistical Inference. Journal of the American Statistical Association: Vol. 103, No. 481, pp. 425-426.

相关与协方差矩阵的惩罚正态似然和岭正则化AI 解读

High dimensionality causes problems in various areas of statistics. A particular situation that rarely has been considered is the testing of hypotheses about multivariate regression models in which the dimension of the multivariate response is large. In this article a ridge regularization approach is proposed in which either the covariance or the correlation matrix is regularized to ensure nonsingularity irrespective of the dimensionality of the data. It is shown that the…

通过序贯检验一致改进 Bonferroni 类检验AI 解读

We propose a new method to uniformly improve cutoff tests (as, e.g., the Bonferroni test) by replacing them with a sequential testing procedure. The proposed procedure provides a uniform improvement—that is, it rejects the null hypothesis at least in all cases where the original procedure does, has level α, and never requires a sample size larger than that of the original procedure. Information on the often-unknown joint distribution of the test statistics across the…

生存结局下预后生物标志物的时间依赖预测值AI 解读

In a prospective cohort study, information on clinical parameters, tests and molecular markers is often collected. Such information is useful to predict patient prognosis and to select patients for targeted therapy. We propose a new graphical approach, the positive predictive value (PPV) curve, to quantify the predictive accuracy of prognostic markers measured on a continuous scale with censored failure time outcome. The proposed method highlights the need to consider both…

配对病例对照研究中条件似然与随机效应似然的等价性AI 解读

Two approaches dominate the analysis of highly stratified data sets: use of conditional likelihood and random-effects models. Conditioning is more traditional and has attractive robustness properties. Contemporary random-effects approaches rely more on accurate assumptions but can be applied to a larger class of problems. For pair-matched studies with arbitrary numbers of categorical covariates, we reconcile these two approaches, showing that the conditional approach has an…

热带非洲罗阿丝虫患病率的二元二项空间建模AI 解读

We present a state-of-the-art application of smoothing for dependent bivariate binomial spatial data to Loa loa prevalence mapping in West Africa. This application starts with the nonspatial calibration of survey instruments, continues with the spatial model building and assessment, and ends with robust, tested software intended for use by field workers for online prevalence map updating. From a statistical perspective, we address several important methodological issues:…

利用空间经验似然进行变差函数模型的点估计与区间估计AI 解读

We present a spatial blockwise empirical likelihood method for estimating variogram model parameters in the analysis of spatial data on a grid. The method produces point estimators that require no spatial variance estimates to compute, unlike least squares methods for variogram fitting, but are as efficient as the best least squares estimator in large samples. Our approach also produces confidence regions for the variogram, without requiring knowledge of the full joint…

控制错误发现率的层级检验方法AI 解读

We discuss methodology for controlling the false discovery rate (FDR) in complex large-scale studies that involve testing multiple families of hypotheses; the tested hypotheses are arranged in a tree of disjoint subfamilies, and the subfamilies of hypotheses are hierarchically tested by the Benjamini and Hochberg FDR-controlling (BH) procedure. We derive an approximation for the multiple family FDR for independently distributed test statistics: q, the level at which the BH…

矩阵折棒过程AI 解读

In analyzing data from multiple related studies, it often is of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonly used, of concern is sensitivity to the form chosen for the random-effects distribution. A Dirichlet process (DP) prior can allow the distribution to be unknown, while clustering studies; however, the DP does not allow local clustering of studies with respect to a subset of the…

利用 SIMEX 选择变量含误差问题中的平滑参数AI 解读

SIMEX methods are attractive for solving curve estimation problems in errors-in-variables regression, using parametric or semiparametric techniques. However, nonparametric approaches are generally of quite a different type, being based on, for example, kernels, local-linear modeling, ridging, orthogonal series, or splines. All of these techniques involve the challenging (and not well studied) issue of empirical smoothing parameter choice. We show that SIMEX can be used…