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插补调查数据的 Bootstrap 方法AI 解读

Most surveys use imputation to compensate for missing data. However, treating the imputed data set as the complete data set and directly applying existing methods (e.g., the linearization, the jackknife, and the bootstrap) for variance estimation and/or statistical inference does not produce valid results, because these methods do not account for the effect of missing data and/or imputation. In this article we show that correct bootstrap estimates can be obtained by…

非参数重要性抽样AI 解读

Importance sampling is a widely used variance reduction simulation technique for the evaluation of high-dimensional integrals. A key step in the implementation of importance sampling is to choose a proper distribution function from which pseudorandom numbers are generated. Parametric sampling distributions, if available at all, are often inadequate for high-dimensional integrals over irregular regions. One possible remedy is to use a nonparametric method to estimate the…

通过正交化模型混合进行预测AI 解读

We introduce an approach and algorithms for model mixing in large prediction problems with correlated predictors. We focus on the choice of predictors in linear models, and mix over possible subsets of candidate predictors. Our approach is based on expressing the space of models in terms of an orthogonalization of the design matrix. Advantages are both statistical and computational. Statistically, orthogonalization often leads to a reduction in the number of competing models…

Lehmann 备择下的等效性检验与区间检验AI 解读

Equivalence and interval tests for Lehmann's alternative that extend the well-known Savage test for one-sided hypotheses are proposed. The proposed tests are shown to be unbiased with a strictly unimodal power function, provided the sample sizes in both treatment groups are equal. By means of a numerical investigation of the bias in the case of unequal sample sizes that are not too far apart, the suggested tests still turn out to provide practicable solutions. Because the…

左截断与右删失数据的非参数估计及回归分析AI 解读

In many prospective and retrospective studies, survival data are subject to left truncation in addition to the usual right censoring. For left-truncated data without covariates, only the conditional distribution of the survival time Y given Y ≥ τ can be estimated nonparametrically, where τ is the lower boundary of the support of the left-truncation variable T. If the data are also right censored, then the conditional distribution can be consistently estimated only at points…

右截断数据的二元估计AI 解读

Bivariate estimation with survival data has received considerable attention recently; however, most of the work has focused on random censoring models. Another common feature of survival data, random truncation, is considered in this study. Truncated data may arise if the time origin of the events under study precedes the observation period. In a random right-truncation model, one observes the iid samples of (Y, T) only if (Y ≤ T), where Y is the variable of interest and T…

利用高斯混合平滑估计混合分布AI 解读

We propose a method of estimating mixing distributions using maximum likelihood over the class of arbitrary mixtures of Gaussians subject to the constraint that the component variances be greater than or equal to some minimum value h. This approach can lead to estimates of many shapes, with smoothness controlled by parameter h. We show that the resulting estimate will always be a finite mixture of Gaussians, each having variance h. The nonparametric maximum likelihood…

有序 Probit 模型的有效估计AI 解读

This article discusses a model in which both the dependent variable and the explanatory variables are ordinal and have an arbitrary number of categories. Assuming joint normality of the underlying continuous latent variables, we compare estimation based on the joint distribution to estimation based on the conditional distribution. Because the explanatory variables are not weakly exogenous in this model, the latter approach implies a loss in efficiency that can be substantial…

随机序约束下生存概率的贝叶斯推断AI 解读

In the statistical analysis of survival data arising from two populations, it often happens that the analyst knows, a priori, that the life lengths in one population are stochastically shorter than those in the other. Nevertheless, survival probability estimates, if determined separately from the corresponding samples, may not be consistent with this prior assumption, because of inherent statistical variability in the observations. This problem has been considered in a…

基于方向邻域的含噪图像上下文分类AI 解读

The directional neighborhoods approach (DNA) to classifying pixels and reconstructing images from remotely sensed noisy data is a newly proposed computer-intensive procedure that is partly Bayesian and partly data analytic. It uses the observational data to select an optimal, generally asymmetric, but relatively homogeneous neighborhood for contextually classifying pixels. A criterion for “homogeneity of neighborhood” is developed. DNA involves two stages: a zero-neighbor…

多元数据中的异常值识别AI 解读

New insights are given into why the problem of detecting multivariate outliers can be difficult and why the difficulty increases with the dimension of the data. Significant improvements in methods for detecting outliers are described, and extensive simulation experiments demonstrate that a hybrid method extends the practical boundaries of outlier detection capabilities. Based on simulation results and examples from the literature, the question of what levels of contamination…

通过生成函数计算置换分布及离散数据敏感性分析AI 解读

Generating functions provide a simple and elegant way to describe probability or frequency distributions of discrete statistics and, in particular, permutation distributions. They are also a computational tool. Many efficient algorithms, including those described as fast Fourier transform methods, network methods, and multiple shift methods, are different implementations of the recursions needed to evaluate generating functions efficiently. Our goals here are twofold. First,…

多元分类响应模型拟合优度统计量的分解AI 解读

Numerical and asymptotic stochastic partitioning of goodness-of-fit statistics are considered for a broad class of simultaneous multivariate categorical response models. These simultaneous models impose constraints on the joint and marginal distributions of categorical response variables. Under certain conditions, the tenability of the corresponding simultaneous hypothesis can be assessed by separately testing the two subhypotheses: one regarding the joint distributions and…

多离散度分量广义线性混合模型的偏差校正AI 解读

General formulas are derived for the asymptotic bias in regression coefficients and variance components estimated by penalized quasi-likelihood (PQL) in generalized linear mixed models with canonical link function and multiple sets of independent random effects. Easily computed correction matrices result in variance component estimates that have satisfactory asymptotic behavior for small values of the variance components and significantly reduce bias for larger values. Both…

极大似然分析中的带符号根偏差剖面与置信区间AI 解读

We investigate a natural extension of the profile t plot of Bates and Watts to a general parametric function g(θ) of the parameters θ in a general maximum likelihood analysis. Although the basic purpose of the extension, called the signed root deviance profile (SRDP), is to construct likelihood ratio (LR) confidence intervals for g(θ), it has various other applications that significantly extend its usefulness. The tangent to the plot of the SRDP at the maximum likelihood…

二元响应回归的图形方法AI 解读

Central dimension-reduction subspaces, which characterize the dependence of a response variable on one or more predictors, are developed and then used to guide the construction and interpretation of graphics for regression problems with a binary response variable. Graphical methods requiring neither a link function nor residuals are suggested for both development and criticism of model components implied by the central dimension-reduction subspace.

评估重复访调查方法有效性的程序：劳动力数据应用AI 解读

Several types of reinterview surveys have been designed to measure the different kinds of response errors that occur in survey data. This article is based on the reinterview procedures conducted by the U.S. Census Bureau in its monthly Current Population Survey. We present estimates of the misclassification error rates associated with the original survey data collection procedures and the reinterview methods. A new procedure enables us to estimate the dependence between the…

二元结局与有序依从性安慰剂对照试验的因果推断AI 解读

We propose a likelihood-based method to analyze the causal effect of partial compliance (i.e., unplanned partial exposure to treatment or placebo) in the LRC-CPPT data, a prevention trial with long term follow-up previously analyzed by Efron and Feldman. Initially, we construct ordered compliance categories and dichotomize response. Assuming increased exposure to cholestyramine does not increase cholesterol, we estimate exposure—response curves in different compliance…

统计决策理论导论AI 解读

The Bayesian revolution in statistics -- where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine -- is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty. Starting with an extensive account of the foundations of decision theory, the…

摘要暂缺

环境时间序列的非线性可加模型：地面臭氧数据应用AI 解读

Environmental time series usually vary systematically in response to meteorological conditions and thus often are not stationary. In this article a class of additive models are introduced for environmental time series, in which both mean levels and variances of the series are nonlinear functions of relevant meteorological variables. Backfitting algorithms in nonlinear regression are adopted to estimate the unknown functions in the model, and the maximum likelihood method is…

摘要暂缺

通过正交化模型混合进行预测AI 解读

We introduce an approach and algorithms for model mixing in large prediction problems with correlated predictors. We focus on the choice of predictors in linear models, and mix over possible subsets of candidate predictors. Our approach is based on expressing the space of models in terms of an orthogonalization of the design matrix. Advantages are both statistical and computational. Statistically, orthogonalization often leads to a reduction in the number of competing models…

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