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约束长期行为的先验分布AI 解读

We propose a class of prior distributions that discipline the long-run behavior of vector autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the…

序列相关函数数据的贝叶斯半参数函数混合模型：青光眼数据应用AI 解读

Glaucoma, a leading cause of blindness, is characterized by optic nerve damage related to intraocular pressure (IOP), but its full etiology is unknown. Researchers at UAB have devised a custom device to measure scleral strain continuously around the eye under fixed levels of IOP, which here is used to assess how strain varies around the posterior pole, with IOP, and across glaucoma risk factors such as age. The hypothesis is that scleral strain decreases with age, which…

多层矩阵变量分析及其在临床人群加速度计活动测量中的应用AI 解读

The number of studies where the primary measurement is a matrix is exploding. In response to this, we propose a statistical framework for modeling populations of repeatedly observed matrix-variate measurements. The 2D structure is handled via a matrix-variate distribution with decomposable row/column-specific covariance matrices and a linear mixed effect framework is used to model the multilevel design. The proposed framework flexibly expands to accommodate many common…

癌种特异性发病年龄外显率的贝叶斯半参数估计：Li-Fraumeni综合征应用AI 解读

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration…

析因实验中的因果交互作用：联合分析应用AI 解读

We study causal interaction in factorial experiments, in which several factors, each with multiple levels, are randomized to form a large number of possible treatment combinations. Examples of such experiments include conjoint analysis, which is often used by social scientists to analyze multidimensional preferences in a population. To characterize the structure of causal interaction in factorial experiments, we propose a new causal interaction effect, called the average…

观察性研究中代表性干预下风险的逆概率加权估计AI 解读

Researchers are often interested in using observational data to estimate the effect on a health outcome of maintaining a continuous treatment within a pre-specified range over time; e.g. "always exercise at least 30 minutes per day". There may be many precise interventions that could achieve this range. In this paper we consider representative interventions. These are special cases of random dynamic interventions; interventions under which treatment at each time is assigned…

通过得分投影实现可解释的高维推断：神经影像应用AI 解读

In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the associations between the outcome and summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify…

分布变化的序贯非参数检验：放射性异常检测应用AI 解读

We propose a sequential nonparametric test for detecting a change in distribution, based on windowed Kolmogorov–Smirnov statistics. The approach is simple, robust, highly computationally efficient, easy to calibrate, and requires no parametric assumptions about the underlying null and alternative distributions. We show that both the false-alarm rate and the power of our procedure are amenable to rigorous analysis, and that the method outperforms existing sequential testing…

高维贝叶斯向量自回归模型的后验一致性AI 解读

Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional genomics and neuroscience. These applications have also accentuated the need to investigate the behavior of the VAR model in a high-dimensional regime, which provides novel insights into the role of temporal dependence for…

基于增量倾向得分干预的非参数因果效应AI 解读

Most work in causal inference considers deterministic interventions that set each unit’s treatment to some fixed value. However, under positivity violations these interventions can lead to nonidentification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these…

变系数部分线性模型的Mallows型模型平均估计AI 解读

In the last decade, significant theoretical advances have been made in the area of frequentist model averaging (FMA); however, the majority of this work has emphasized parametric model setups. This article considers FMA for the semiparametric varying-coefficient partially linear model (VCPLM), which has gained prominence to become an extensively used modeling tool in recent years. Within this context, we develop a Mallows-type criterion for assigning model weights and prove…

一种通用的确定独立筛选程序AI 解读

Wenliang Pana, Xueqin Wanga, Weinan Xiaoa & Hongtu Zhub*a Department of Statistical Science, School of Mathematics, Sun Yat-Sen University, Guangzhou, P. R. Chinab Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXCONTACT Hongtu Zhu hzhu5@mdanderson.org Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030Supplementary materials for this article are available online. Please go to…

调查中不可忽略单位无应答的工具校准：一则警示AI 解读

Response rates have been steadily declining over the last decades, making survey estimates vulnerable to nonresponse bias. To reduce the potential bias, two weighting approaches are commonly used in National Statistical Offices: the one-step and the two-step approaches. In this article, we focus on the one-step approach, whereby the design weights are modified in a single step with two simultaneous goals in mind: reduce the nonresponse bias and ensure the consistency between…

高频跳跃检验的自助法AI 解读

The main contribution of this article is to propose a bootstrap test for jumps based on functions of realized volatility and bipower variation. Bootstrap intraday returns are randomly generated from a mean zero Gaussian distribution with a variance given by a local measure of integrated volatility (which we denote by {v^in}). We first discuss a set of high-level conditions on {v^in} such that any bootstrap test of this form has the correct asymptotic size and is…

基于成分调整阈值法的高维成分数据协方差估计AI 解读

Yuanpei Caoa, Wei Linb & Hongzhe Lia*a Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PAb School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, ChinaCONTACT Hongzhe Li hongzhe@upenn.edu Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.Color versions of one or more of the figures in the…

过度乐观：经 SURE 调参后的表观误差有多大偏差？AI 解读

Nearly all estimators in statistical prediction come with an associated tuning parameter, in one way or another. Common practice, given data, is to choose the tuning parameter value that minimizes a constructed estimate of the prediction error of the estimator; we focus on Stein’s unbiased risk estimator, or SURE, which forms an unbiased estimate of the prediction error by augmenting the observed training error with an estimate of the degrees of freedom of the estimator.…

病例—对照点模式数据的随机拟似然AI 解读

We propose a novel stochastic quasi-likelihood estimation procedure for case-control point processes. Quasi-likelihood for point processes depends on a certain optimal weight function and for the new method the weight function is stochastic since it depends on the control point pattern. The new procedure also provides a computationally efficient implementation of quasi-likelihood for univariate point processes in which case a synthetic control point process is simulated by…

通过高效数据子抽样加速 MCMCAI 解读

We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where the likelihood function for n observations is estimated from a random subset of m observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to…

成对匹配观察性研究的敏感性值AI 解读

This article proposes a new quantity called the “sensitivity value,” which is defined as the minimum strength of unmeasured confounders needed to change the qualitative conclusions of a naive analysis assuming no unmeasured confounder. We establish the asymptotic normality of the sensitivity value in pair-matched observational studies. The theoretical results are then used to approximate the power of a sensitivity analysis and select the design of a study. We explore the…

通过特征分析识别协整AI 解读

We propose a new and easy-to-use method for identifying cointegrated components of nonstationary time series, consisting of an eigenanalysis for a certain non-negative definite matrix. Our setting is model-free, and we allow the integer-valued integration orders of the observable series to be unknown, and to possibly differ. Consistency of estimates of the cointegration space and cointegration rank is established both when the dimension of the observable time series is fixed…

高维时间序列的趋势检验AI 解读

The article considers statistical inference for trends of high-dimensional time series. Based on a modified L2 distance between parametric and nonparametric trend estimators, we propose a de-diagonalized quadratic form test statistic for testing patterns on trends, such as linear, quadratic, or parallel forms. We develop an asymptotic theory for the test statistic. A Gaussian multiplier testing procedure is proposed and it has an improved finite sample performance. Our…

社区数量未知时的概率社区检测AI 解读

A fundamental problem in network analysis is clustering the nodes into groups which share a similar connectivity pattern. Existing algorithms for community detection assume the knowledge of the number of clusters or estimate it a priori using various selection criteria and subsequently estimate the community structure. Ignoring the uncertainty in the first stage may lead to erroneous clustering, particularly when the community structure is vague. We instead propose a…

cmenet：条件主效应的双层变量选择方法AI 解读

Simon Maka & C. F. Jeff Wua*a School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GACONTACT C. F. Jeff Wu jeffwu@isye.gatech.edu School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332.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…

含协变量的有向网络模型中的统计推断AI 解读

Ting Yana http://orcid.org/0000-0001-9830-6646, Binyan Jiangb, Stephen E. Fienbergc* & Chenlei Lengd*a Department of Statistics, Central China Normal University, Wuhan, Chinab Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, Chinac Department of Statistics, Heinz College, Machine Learning Department, Cylab, Carnegie Mellon University, Pittsburgh, PAd Department of Statistics, University of Warwick and Alan Turing Institute, Coventry, UKCONTACT…