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REACT 散点图平滑器：通过基表示的简约性实现超效率AI 解读

REACT estimators for the mean of a linear model involve three steps: transforming the model to a canonical form that provides an economical representation of the unknown mean vector, estimating the risks of a class of candidate linear shrinkage estimators, and adaptively selecting the candidate estimator that minimizes estimated risk. Applied to one- or higher-way layouts, the REACT method generates automatic scatterplot smoothers that compete well on standard datasets with…

使用条件均值插补数据的统计推断AI 解读

In this article we present analytic techniques for inference from a dataset in which missing values have been replaced by predictive means derived from an imputation model. The derivations are based on asymptotic expansions of point estimators and their associated variance estimators, and the resulting formulas can be thought of as first-order approximations to standard multiple-imputation procedures with an infinite number of imputations for the missing values. Our method,…

安全且有效的重要性抽样AI 解读

We present two improvements on the technique of importance sampling. First, we show that importance sampling from a mixture of densities, using those densities as control variates, results in a useful upper bound on the asymptotic variance. That bound is a small multiple of the asymptotic variance of importance sampling from the best single component density. This allows one to benefit from the great variance reductions obtainable by importance sampling, while protecting…

Metropolis 抽样中的多重尝试方法与局部优化AI 解读

This article describes a new Metropolis-like transition rule, the multiple-try Metropolis, for Markov chain Monte Carlo (MCMC) simulations. By using this transition rule together with adaptive direction sampling, we propose a novel method for incorporating local optimization steps into a MCMC sampler in continuous state-space. Numerical studies show that the new method performs significantly better than the traditional Metropolis-Hastings (M-H) sampler. With minor tailoring…

条件 Logistic 回归的高效蒙特卡洛方法AI 解读

Exact inference for the logistic regression model is based on generating the permutation distribution of the sufficient statistics for the regression parameters of interest conditional on the sufficient statistics for the remaining (nuisance) parameters. Despite the availability of fast numerical algorithms for the exact computations, there are numerous instances where a data set is too large to be analyzed by the exact methods, yet too sparse or unbalanced for the maximum…

荟萃分析中校正发表偏倚的非参数“剪补法”AI 解读

Meta-analysis collects and synthesizes results from individual studies to estimate an overall effect size. If published studies are chosen, say through a literature review, then an inherent selection bias may arise, because, for example, studies may tend to be published more readily if they are statistically significant, or deemed to be more “interesting” in terms of the impact of their outcomes. We develop a simple rank-based data augmentation technique, formalizing the use…

二元失效时间数据的模型选择与半参数推断AI 解读

We propose model selection procedures for bivariate survival models for censored data generated by the Archimedean copula family. In route to constructing the selection methodology, we develop estimates of some time-dependent association measures, including estimates of the local and global Kendall's tau, local odds ratio, and other measures defined throughout the literature. We propose a goodness-of-fit-based model selection methodology as well as a graphical approach. We…

主研究与验证研究设计下含误分类和测量误差的 Logistic 回归推断AI 解读

In epidemiological studies, continuous covariates often are measured with error and categorical covariates often are misclassified. Using the logistic regression model to represent the relationship between the binary outcome and the perfectly measured and classified covariates, the model for the observed main study data is derived. This derivation relies on the assumption that the error in the continuous covariates is multivariate normally distributed and uses a chain of…

含缺失二元结局、辅助变量与全有或全无依从性的随机癌症预防试验分析AI 解读

The Prostate Cancer Prevention Trial is a randomized chemoprevention trial designed to compare the effect of daily finasteride versus placebo on prostate cancer determined by biopsy. Investigators have scheduled a biopsy at the end of the trial in 7 years or following a positive prostate-specific antigen (PSA) on annual screening. The analysis will need to adjust for two likely complications. First, some subjects will not receive a biopsy, depending in part on whether or not…

转移回归模型及其在环境时间序列中的应用AI 解读

Environmental epidemiologists often encounter time series data in the form of discrete or other nonnormal outcomes; for example, in modeling the relationship between air pollution and hospital admissions or mortality rates. We present a case study examining the association between pollen counts and meteorologic covariates. Although such time series data are inadequately described by standard methods for Gaussian time series, they are often autocorrelated, and warrant an…

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马尔可夫链蒙特卡洛：用于贝叶斯推断的随机模拟AI 解读

Introduction Stochastic simulation Introduction Generation of Discrete Random Quantities Generation of Continuous Random Quantities Generation of Random Vectors and Matrices Resampling Methods Exercises Bayesian Inference Introduction Bayes' Theorem Conjugate Distributions Hierarchical Models Dynamic Models Spatial Models Model Comparison Exercises Approximate methods of inference Introduction Asymptotic Approximations Approximations by Gaussian Quadrature Monte Carlo…

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稀有事件生存数据的一类加权对数秩检验AI 解读

In many epidemiological and medical follow-up studies, a majority of study subjects do not experience the event of interest during the follow-up period. An important example is the ongoing prostate, lung, colorectal, and ovarian cancer Screening trial of the National Cancer Institute. In such a situation, the widely used Gp family of weighted log-rank statistics essentially reduces to the special case of the (unweighted) log-rank statistics. We propose a simple modification…

REACT 散点平滑：通过简约基表示实现超效率AI 解读

REACT estimators for the mean of a linear model involve three steps: transforming the model to a canonical form that provides an economical representation of the unknown mean vector, estimating the risks of a class of candidate linear shrinkage estimators, and adaptively selecting the candidate estimator that minimizes estimated risk. Applied to one- or higher-way layouts, the REACT method generates automatic scatterplot smoothers that compete well on standard datasets with…

基于条件均值填补数据的统计推断AI 解读

In this article we present analytic techniques for inference from a dataset in which missing values have been replaced by predictive means derived from an imputation model. The derivations are based on asymptotic expansions of point estimators and their associated variance estimators, and the resulting formulas can be thought of as first-order approximations to standard multiple-imputation procedures with an infinite number of imputations for the missing values. Our method,…