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空间信号的错误发现率控制AI 解读

The problem of multiple testing for the presence of signal in spatial data can involve numerous locations. Traditionally, each location is tested separately for signal presence, but then the findings are reported in terms of clusters of nearby locations. This is an indication that the units of interest for testing are clusters rather than individual locations. The investigator may know a priori these more natural units or an approximation to them. We suggest testing these…

关于《马尔可夫链蒙特卡洛的固定宽度输出分析》的若干评论AI 解读

Our aim is to relax assumptions and simplify proofs in results given by Jones, Haran, Caffo, and Neath in the recent paper “Fixed-Width Output Analysis for Markov Chain Monte Carlo.”

信息性观测与删失时间下的纵向数据回归分析AI 解读

Longitudinal data frequently occur in many studies, such as longitudinal follow-up studies. To develop statistical methods and theory for the analysis of these data, independent or noninformative observation and censoring times are typically assumed, which naturally leads to inference procedures conditional on observation and censoring times. But in many situations this may not be true or realistic; that is, longitudinal responses may be correlated with observation times as…

由纵向分类数据估计事件发生时间AI 解读

The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing of one point of EDSS (relative progression). Survival methods for time to progression are not adequate for such data since they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated…

无残疾预期寿命的估计AI 解读

A rapidly aging population, such as the United States today, is characterized by the increased prevalence of chronic impairment. Robust estimation of disability-free life expectancy (DFLE), or healthy life expectancy, is essential for examining whether additional years of life are spent in good health and whether life expectancy is increasing faster than the decline of disability rates. Over 30 years since its publication, Sullivan's method remains the most widely used…

随机对照试验中他莫昔芬预防乳腺癌效应的推断AI 解读

The largest randomized, double-blind, placebo-controlled chemoprevention trial, the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (NSABP-BCPT), evaluated the efficacy of tamoxifen in the prevention of breast cancer among women at high risk of developing the disease. The trial has reported a reduction of breast cancer incidence for the tamoxifen group. However, the effect of tamoxifen on the time to diagnosis of the disease over the…

比较肿瘤发生率时纳入历史对照数据AI 解读

Animal carcinogenicity studies, such as those conducted by the U.S. National Toxicology Program (NTP), focus on detecting trends in tumor rates across dose groups. Over time, the NTP has compiled vast amounts of data on tumors in control animals. Currently, this information is used informally, without the benefit of statistical tests for carcinogenicity that directly incorporate historical data on control animals. This article proposes a survival-adjusted test for detecting…

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随机场中的稀疏信号检测及其脑图谱应用AI 解读

Brain mapping data have been modeled as Gaussian random fields, and local increases in mean are detected by local maxima of a random field of test statistics derived from these data. Accurate p values for local maxima are readily available for isotropic data based on the expected Euler characteristic of the excursion set of the test statistic random field. In this article we give a simple method for dealing with nonisotropic data. Our approach has connections to the model of…

用于评估海洋混合层深度趋势的主观似然AI 解读

This article describes a Bayesian statistical analysis of long-term changes in the depth of the ocean's mixed layer. The data are thermal profiles recorded by ships. For these data, there is no good sampling model and thus no obvious likelihood function. Our approach is to elicit posterior distributions for training data directly from the expert. We then infer the likelihood function and use it on large datasets.

用于连锁分析 p 值估计的重要性抽样AI 解读

Importance sampling methods are proposed for estimating the probability that the maximum of a random process exceeds a high threshold, with particular attention to assessing genome-wide significance levels in linkage analysis. The proposed algorithm is applied to computing the conditional significance level, given observed phenotypes, of scan statistics to map quantitative traits in experimental populations and in extended pedigrees of moderate size with partially…

具有过度识别约束的工具变量回归敏感性分析AI 解读

Instrumental variables regression (IV regression) is a method for making causal inferences about the effect of a treatment based on an observational study in which there are unmeasured confounding variables. The method requires one or more valid instrumental variables (IVs); a valid IV is a variable that is associated with the treatment, is independent of unmeasured confounding variables, and has no direct effect on the outcome. Often there is uncertainty about the validity…

函数型主成分回归与函数型偏最小二乘AI 解读

Regression of a scalar response on signal predictors, such as near-infrared (NIR) spectra of chemical samples, presents a major challenge when, as is typically the case, the dimension of the signals far exceeds their number. Most solutions to this problem reduce the dimension of the predictors either by regressing on components [e.g., principal component regression (PCR) and partial least squares (PLS)] or by smoothing methods, which restrict the coefficient function to the…

广义线性模型类推断的稳健建模AI 解读

Generalized linear models (GLMs) are widely used for data analysis; however, their maximum likelihood estimators can be sensitive to outliers. We propose new statistical models that allow robust inferences from the GLM class of models, including Poisson and binomial GLMs, and their extension to generalized linear mixed models. The likelihood score equations from the new models give estimators with bounded influence, so that the resulting estimators are robust against…

结果依赖型随访的纵向研究AI 解读

We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing…

多元时间序列间不相关性的最优检验AI 解读

The problem of testing noncorrelation between two multivariate time series is considered. Assuming that the global process admits a joint vector autoregressive (VAR) representation, noncorrelation between the two component series is equivalent to the hypothesis that all off-diagonal blocks in the matrix coefficients and the innovation covariance of the joint VAR representation are zero. We establish an adequate local asymptotic normality (LAN) property for this VAR model in…

利用 MCMC 实现基于估计函数的推断AI 解读

Under a semiparametric or nonparametric setting, inferences about the unknown parameter are often made based on a nonsmooth estimating function. Resampling methods are quite handy for obtaining good approximations to the distribution of the consistent estimator when the estimating equation and its resampled counterparts are not difficult to solve numerically. In this article we propose a simple, flexible procedure that provides such approximations through the standard Markov…

有限混合回归模型中的变量选择AI 解读

In the applications of finite mixture of regression (FMR) models, often many covariates are used, and their contributions to the response variable vary from one component to another of the mixture model. This creates a complex variable selection problem. Existing methods, such as the Akaike information criterion and the Bayes information criterion, are computationally expensive as the number of covariates and components in the mixture model increases. In this article we…

控制错误发现率的理想与自适应复合决策规则AI 解读

We develop a compound decision theory framework for multiple-testing problems and derive an oracle rule based on the z values that minimizes the false nondiscovery rate (FNR) subject to a constraint on the false discovery rate (FDR). We show that many commonly used multiple-testing procedures, which are p value–based, are inefficient, and propose an adaptive procedure based on the z values. The z value–based adaptive procedure asymptotically attains the performance of the z…

用于降维的方向回归AI 解读

We introduce directional regression (DR) as a method for dimension reduction. Like contour regression, DR is derived from empirical directions, but achieves higher accuracy and requires substantially less computation. DR naturally synthesizes the dimension reduction estimators based on conditional moments, such as sliced inverse regression and sliced average variance estimation, and in doing so combines the advantages of these methods. Under mild conditions, it provides…

通过最小二乘近似统一 LASSO 估计AI 解读

We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Specifically, LSA can transfer many different types of LASSO objective functions into their asymptotically equivalent least squares problems. Thereafter, the standard asymptotic theory can be established and the LARS algorithm can…

最优地统计模型选择AI 解读

In many fields of science, predicting variables of interest over a study region based on noisy data observed at some locations is an important problem. Two popular methods for the problem are kriging and smoothing splines. The former assumes that the underlying process is stochastic, whereas the latter assumes it is purely deterministic. Kriging performs better than smoothing splines in some situations, but is outperformed by smoothing splines in others. However, little is…

基于小波的贝叶斯曲线分类AI 解读

We propose classification models for binary and multicategory data where the predictor is a random function. We use Bayesian modeling with wavelet basis functions that have nice approximation properties over a large class of functional spaces and can accommodate a wide variety of functional forms observed in real life applications. We develop an unified hierarchical model to encompass both the adaptive wavelet-based function estimation model and the logistic classification…