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预测误差的估计AI 解读

Having constructed a data-based estimation rule, perhaps a logistic regression or a classification tree, the statistician would like to know its performance as a predictor of future cases. There are two main theories concerning prediction error: (1) penalty methods such as Cp, Akaike's information criterion, and Stein's unbiased risk estimate that depend on the covariance between data points and their corresponding predictions; and (2) cross-validation and related…

提高病例队列研究中相对风险估计的效率AI 解读

The case-cohort design is a common means of reducing the cost of covariate measurements in large failure-time studies. Under this design, complete covariate data are collected only on the cases (i. e., the subjects whose failure times are uncensored) and on a subcohort randomly selected from the whole cohort. In many applications, certain covariates are readily measured on all cohort members, and surrogate measurements of the expensive covariates also may be available. The…

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面向单侧备择的条件分布无关多元符号检验AI 解读

We consider the problem of testing the hypothesis that a multivariate location vector is in the positive orthant. A conditionally distribution-free sign test is proposed for this problem. This test is related to the Hodges test and can be motivated by the union–intersection principle. Moreover, it is valid under very mild assumptions. A characterization of the conditional null distribution of the test statistic is given. We provide a step-by-step procedure that can be used…

通过加权后验核密度估计计算状态空间蒙特卡洛似然AI 解读

Maximum likelihood estimation and likelihood ratio tests for nonlinear, non-Gaussian state-space models require numerical integration for likelihood calculations. Several methods, including Monte Carlo (MC) expectation maximization, MC likelihood ratios, direct MC integration, and particle filter likelihoods, are inefficient for the motivating problem of stage-structured population dynamics models in experimental settings. An MC kernel likelihood (MCKL) method is presented…

建模还是不建模？有限总体抽样的两种推断方式AI 解读

Finite population sampling is perhaps the only area of statistics in which the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measured variables. This article reviews the debate between design-based and model-based inference. The basic features of the two approaches are illustrated using the case of inference about the mean from stratified random samples. Strengths and weakness of design-based and model-based…

响应随机缺失时的半参数回归分析AI 解读

We develop inference tools in a semiparametric partially linear regression model with missing response data. A class of estimators is defined that includes as special cases a semiparametric regression imputation estimator, a marginal average estimator, and a (marginal) propensity score weighted estimator. We show that any of our class of estimators is asymptotically normal. The three special estimators have the same asymptotic variance. They achieve the semiparametric…

The partially linear model Y = XTβ + ν(Z) + ϵ has been studied extensively when data are completely observed. In this article, we consider the case where the covariate X is sometimes missing, with missingness probability π depending on (Y, Z). New methods are developed for estimating β and ν(·). Our methods are shown to outperform asymptotically methods based only on the complete data. Asymptotic efficiency is discussed, and the semiparametric efficient score function is…

异方差单因素方差分析与失拟检验AI 解读

Recent articles have considered the asymptotic behavior of the one-way analysis of variance (ANOVA) F statistic when the number of levels or groups is large. In these articles, the results were obtained under the assumption of homoscedasticity and for the case when the sample or group sizes ni remain fixed as the number of groups, a, tends to infinity. In this article, we study both weighted and unweighted test statistics in the heteroscedastic case. The unweighted statistic…

用于诊断相关结果线性模型正态误差的 Cholesky 残差AI 解读

Despite the widespread popularity of linear models for correlated outcomes (e.g., linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this article we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated marginal residual vector by the Cholesky decomposition of the inverse of the estimated marginal variance…

多个线性回归模型的多重比较AI 解读

Research on multiple comparison during the past 50 years or so has focused mainly on the comparison of several population means. Several years ago, Spurrier considered the multiple comparison of several simple linear regression lines. He constructed simultaneous confidence bands for all of the contrasts of the simple linear regression lines over the entire range (-∞, ∞) when the models have the same design matrices. This article extends Spurrier's work in several directions.…

基于熵过滤的远缘蛋白质基序搜索贝叶斯插入与删除算法AI 解读

Bayesian models have been developed that find ungapped motifs in multiple protein sequences. In this article, we extend the model to allow for deletions and insertions in motifs. Direct generalization of the ungapped algorithm, based on Gibbs sampling, proved unsuccessful because the configuration space became much larger. To alleviate the convergence difficulty, a two-stage procedure is introduced. At the first stage, we develop a method called entropy filtering, which…

随机缺失协变量回归模型的后验正常化与极大似然估计存在性AI 解读

Characterizing model identifiability in the presence of missing covariate data is a very important issue in missing data problems. In this article, we characterize the propriety of the posterior distribution of the regression coefficients for some general classes of regression models, including the class of generalized linear models (GLM's) and parametric survival models with right-censored data. Toward this goal, we derive some very general and easy-to-check conditions for…

空间相关数据的贝叶斯因子分析及地区物质贫困指数构建AI 解读

This article describes a Bayesian hierarchical model for factor analysis of spatially correlated multivariate data. The first level specifies, for each area on a map, the distribution of a vector of manifest variables conditional on an underlying latent factor; at the second level, the area-specific latent factors have a joint distribution that incorporates spatial correlation. The framework allows for both marginal and conditional (e.g., conditional autoregressive)…

数据挖掘中的变量选择AI 解读

We predict the onset of personal bankruptcy using least squares regression. Although well publicized, only 2,244 bankruptcies occur in our dataset of 2.9 million months of credit-card activity. We use stepwise selection to find predictors of these from a mix of payment history, debt load, demographics, and their interactions. This combination of rare responses and over 67,000 possible predictors leads to a challenging modeling question: How does one separate coincidental…

Rasch模型与匹配病例对照研究中条件法和混合法的等价性及应用AI 解读

Analyses of data using Rasch models, including the special case of matched case-control studies, are common applications of conditional likelihood in which the usual inferential procedures are applied only after conditioning on an approximately ancillary statistic. Another common approach to the analysis of Rasch models is to integrate the nuisance parameters over a mixing distribution, using the marginal likelihood obtained as the basis for inference. We show that the full…

基于竞争模型L²距离的混合分布同质性检验AI 解读

Ascertaining the number of components in a mixture distribution is an interesting and challenging problem for statisticians. Chen, Chen, and Kalbfleisch recently proposed a modified likelihood ratio test (MLRT), which is distribution-free and locally most powerful, asymptotically. In this article we present a new method for testing whether a finite mixture distribution is homogeneous. Our method, the D test, is based on the L2 distance between a fitted homogeneous model and…

二次损失下组合估计问题的半参数基础AI 解读

When there is uncertainty concerning the appropriate statistical model-estimator to use in representing the data sampling process, we consider a basis for optimally combining estimation problems. The objective is to produce natural adaptive estimators that are free of subjective choices and tuning parameters. In the context of two competing multivariate linear statistical models–estimators, we demonstrate a semiparametric Stein-like (SPSL) estimator, , that, under quadratic…

偏斜指数幂分布的统计推断AI 解读

When the analysis of real data indicates that normality assumptions are untenable, more flexible models that cope with the most prevalent deviations from normality can be adopted. In this context, the skew exponential power (SEP) distribution warrants special attention, because it encompasses distributions having both heavy tails and skewness, it allows likelihood inference, and it includes the normal model as a special case. This article concerns likelihood inference about…

广义线性混合模型的稳健分析AI 解读

The method of maximum likelihood (ML) is widely used for analyzing generalized linear mixed models (GLMM's). A full maximum likelihood analysis requires numerical integration techniques for calculation of the log-likelihood, and to avoid the computational problems involving irreducibly high-dimensional integrals, several maximum likelihood algorithms have been proposed in the literature to estimate the model parameters by approximating the log-likelihood function. Although…