日期精度：到日 16589 篇 · 到月 19705 篇 · 到年 2852 篇 · 未提供 26 篇
多元失效时间数据的统计分析：边际建模方法
In large studies of public health interventions, individuals are typically at risk of many different types of events. Despite the multitude of events that may arise, a single failure time is often ...
高维回归中免手工调参的稳健高效方法
We introduce a novel approach for high-dimensional regression with theoretical guarantees. The new procedure overcomes the challenge of tuning parameter selection of Lasso and possesses several appealing properties. It uses an easily simulated tuning parameter that automatically adapts to both the unknown random error distribution and the correlation structure of the design matrix. It is robust with substantial efficiency gain for heavy-tailed random errors while maintaining…
核方法与筛方法结合：稀疏可加模型正则化后的置信带
Junwei Lua, Mladen Kolarb & Han Liuc Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA Booth School of Business, The University of Chicago, Chicago, IL Department of Computer Science, Northwestern University, Evanston, IL Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Junwei Lu junweilu@hsph.harvard.edu Department of Biostatistics, Harvard T. H. Chan School of Public Health,…
利用树状收缩多结局回归估计细颗粒物与432种心血管疾病的关系
The positive relationship between airborne fine particulate matter (PM2.5) and cardiovascular disease (CVD) is established. Little is known about effect size heterogeneity across distinct CVD outcomes. We conducted a multi-outcome case-crossover study of Medicare beneficiaries aged >65 years residing in the mainland USA from 2000 through 2012. The exposure was two-day average PM2.5 in each individual’s residential zipcode. The outcomes were hospitalization for 432 distinct…
基于 IVX-AR 模型检验美国住房价格指数收益的可预测性
We use ten common macroeconomic variables to test for the predictability of the quarterly growth rate of house price index (HPI) in the United States during 1975:Q1–2018:Q2. We extend the instrumental variable based Wald statistic (IVX-KMS) proposed by Kostakis, Magdalinos, and Stamatogiannis to a new instrumental variable based Wald statistic (IVX-AR) which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model.…
桥下的五个巨魔：异步且含噪高频数据的主成分分析
We develop a principal component analysis (PCA) for high frequency data. As in Northern fairy tales, there are trolls waiting for the explorer. The first three trolls are market microstructure noise, asynchronous sampling times, and edge effects in estimators. To get around these, a robust estimator of the spot covariance matrix is developed based on the smoothed two-scale realized variance (S-TSRV). The fourth troll is how to pass from estimated time-varying covariance…
非可忽略未响应下的贝叶斯标量对图像回归
Medical imaging has become an increasingly important tool in screening, diagnosis, prognosis, and treatment of various diseases given its information visualization and quantitative assessment. The aim of this article is to develop a Bayesian scalar-on-image regression model to integrate high-dimensional imaging data and clinical data to predict cognitive, behavioral, or emotional outcomes, while allowing for nonignorable missing outcomes. Such a nonignorable nonresponse…
合成控制方法所估平均处理效应的统计推断
The synthetic control (SC) method, a powerful tool for estimating average treatment effects (ATE), is increasingly popular in fields such as statistics, economics, political science, and marketing. The SC is particularly suitable for estimating ATE with a single (or a few) treated unit(s), a fixed number of control units, and large pre and post-treatment periods (which we refer as “long panels”). To date, there has been no formal inference theory for SC ATE estimator with…
用于电子健康记录表型识别的贝叶斯双重特征分配
Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes - in our case, latent disease - based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases from EHR data. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously…
面向高维特征预测的定向随机投影
Minerva Mukhopadhyaya & David B. Dunsonb Department of Mathematics and Statistics, Indian Institute of Technology, Kanpur, India Department of Statistical Science, Duke University, Durham, NC Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA. Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Minerva Mukhopadhyay minervam@iitk.ac.in Department of…
具有共轭满条件分布的自然指数族相关数据贝叶斯层级模型
We introduce a Bayesian approach for analyzing (possibly) high-dimensional dependent data that are distributed according to a member from the natural exponential family of distributions. This problem requires extensive methodological advancements, as jointly modeling high-dimensional dependent data leads to the so-called “big n problem.” The computational complexity of the “big n problem” is further exacerbated when allowing for non-Gaussian data models, as is the case here.…
利用辅助信息的自适应稀疏估计
The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general framework that involves first using an auxiliary sequence to capture the side information, and then incorporating the auxiliary sequence in inference to reduce the estimation risk. The proposed method, which carries out adaptive Stein’s unbiased risk estimate-thresholding using side information…
非概率调查样本的双重稳健推断
We establish a general framework for statistical inferences with nonprobability survey samples when relevant auxiliary information is available from a probability survey sample. We develop a rigorous procedure for estimating the propensity scores for units in the nonprobability sample, and construct doubly robust estimators for the finite population mean. Variance estimation is discussed under the proposed framework. Results from simulation studies show the robustness and…
带置信保证的交叉验证
Cross-validation is one of the most popular model and tuning parameter selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, due to the ignorance of the uncertainty in the testing sample. We develop a novel statistically principled inference tool based on cross-validation that takes into account the uncertainty in the testing sample. This method outputs a set of highly competitive candidate…
两阶段研究的最优设计
The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under such a design, the outcome and inexpensive covariates are measured on all subjects in the first phase and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase. Previous research on two-phase studies has focused largely…
非平稳时间序列的经验频带分析
The time-varying power spectrum of a time series process is a bivariate function that quantifies the magnitude of oscillations at different frequencies and times. To obtain low-dimensional, parsimonious measures from this functional parameter, applied researchers consider collapsed measures of power within local bands that partition the frequency space. Frequency bands commonly used in the scientific literature were historically derived, but they are not guaranteed to be…
非线性交叉验证预测指标的小样本改进估计
When predicting an outcome is the scientific goal, one must decide on a metric by which to evaluate the quality of predictions. We consider the problem of measuring the performance of a prediction algorithm with the same data that were used to train the algorithm. Typical approaches involve bootstrapping or cross-validation. However, we demonstrate that bootstrap-based approaches often fail and standard cross-validation estimators may perform poorly. We provide a general…
深度 Knockoff 方法
This article introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the produced knockoffs is optimized; this criterion is inspired by the popular maximum mean discrepancy in machine learning and can be thought of as measuring the distance to pairwise exchangeability between…
基于逆概率加权的稳健推断
Inverse probability weighting (IPW) is widely used in empirical work in economics and other disciplines. As Gaussian approximations perform poorly in the presence of “small denominators,” trimming is routinely employed as a regularization strategy. However, ad hoc trimming of the observations renders usual inference procedures invalid for the target estimand, even in large samples. In this article, we first show that the IPW estimator can have different (Gaussian or…
通过期望传播快速准确地分析二元响应混合模型
Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to approximate integrals arising in frequentist statistical inference. We focus on likelihood-based inference for binary response mixed models and show that fast and accurate quadrature-free inference can be realized for the probit link case…
针对新型超球面分布备择的旋转对称性最优检验
Motivated by the central role played by rotationally symmetric distributions in directional statistics, we consider the problem of testing rotational symmetry on the hypersphere. We adopt a semiparametric approach and tackle problems where the location of the symmetry axis is either specified or unspecified. For each problem, we define two tests and study their asymptotic properties under very mild conditions. We introduce two new classes of directional distributions that…
保持不变性的计算机模型统计仿真器：结构能量预测应用
Statistical design and analysis of computer experiments is a growing area in statistics. Computer models with structural invariance properties now appear frequently in materials science, physics, biology, and other fields. These properties are consequences of dependency on structural geometry, and cannot be accommodated by standard statistical emulation methods. In this article, we propose a statistical framework for building emulators to preserve invariance. The framework…
用于子空间收缩的函数型 Horseshoe 先验
We introduce a new shrinkage prior on function spaces, called the functional horseshoe prior (fHS), that encourages shrinkage towards parametric classes of functions. Unlike other shrinkage priors for parametric models, the fHS shrinkage acts on the shape of the function rather than inducing sparsity on model parameters. We study the efficacy of the proposed approach by showing an adaptive posterior concentration property on the function. We also demonstrate consistency of…
基因扰动实验的强信号检测：具有功效保证与 FDR 控制的自适应方法
The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose a modified two-group model where the null group corresponds to genes which are not direct targets, but can have small nonzero effects. We model the behavior of genes from the null set by a Gaussian distribution with unknown variance τ 2 . To…