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图引导的协方差矩阵带状估计AI 解读

Regularization has become a primary tool for developing reliable estimators of the covariance matrix in high-dimensional settings. To curb the curse of dimensionality, numerous methods assume that the population covariance (or inverse covariance) matrix is sparse, while making no particular structural assumptions on the desired pattern of sparsity. A highly-related, yet complementary, literature studies the specific setting in which the measured variables have a known…

无重复矩阵型数据的均值与协方差联合估计AI 解读

Michael Hornsteina*, Roger Fana, Kerby Sheddena & Shuheng Zhouaba Department of Statistics, University of Michigan, Ann Arbor, MIb Department of Statistics, University of California, Riverside, CACONTACT Michael Hornstein mdhorn@umich.edu Department of Statistics, University of Michigan, Ann Arbor, MI 48109.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…

平滑变化均值趋势下的方差变点检测及肝脏获取应用AI 解读

Literature on change point analysis mostly requires a sudden change in the data distribution, either in a few parameters or the distribution as a whole. We are interested in the scenario, where the variance of data may make a significant jump while the mean changes in a smooth fashion. The motivation is a liver procurement experiment monitoring organ surface temperature. Blindly applying the existing methods to the example can yield erroneous change point estimates since the…

含潜变量的高斯条件随机场图模型选择AI 解读

We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as "sparsistent" (i.e., capable of recovering the graph structure).…

大型线性常微分方程系统的参数估计与变量选择：矩阵方法AI 解读

Ordinary differential equations (ODEs) are widely used to model the dynamic behavior of a complex system. Parameter estimation and variable selection for a "Big System" with linear ODEs are very challenging due to the need of nonlinear optimization in an ultra-high dimensional parameter space. In this article, we develop a parameter estimation and variable selection method based on the ideas of similarity transformation and separable least squares (SLS). Simulation studies…

对 PENCOMP 的讨论AI 解读

We would like to congratulate Zhou, Elliott, and Little (henceforth referred to as ZEL) on an interesting article in an important area of research. ZEL propose a new approach for causal inference u...

用于处理比较的倾向得分惩罚样条方法AI 解读

Valid causal inference from observational studies requires controlling for confounders. When time-dependent confounders are present that serve as mediators of treatment effects and affect future treatment assignment, standard regression methods for controlling for confounders fail. Similar issues also arise in trials with sequential randomization, when randomization at later time points is based on intermediate outcomes from earlier randomized assignments. We propose a…

移动应用激活数据的捕获—再捕获方法AI 解读

This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can…

考虑发热杀灭寄生虫与测量误差的疟疾归因发热比例估计AI 解读

Malaria is a major health problem in many tropical regions. Fever is a characteristic symptom of malaria. The fraction of fevers that are attributable to malaria, the malaria attributable fever fraction (MAFF), is an important public health measure in that the MAFF can be used to calculate the number of fevers that would be avoided if malaria was eliminated. Despite such causal interpretation, the MAFF has not been considered in the framework of causal inference. We define…

高维线性模型中遗传关联性的最优估计AI 解读

Estimating the genetic relatedness between two traits based on the genome-wide association data is an important problem in genetics research. In the framework of high-dimensional linear models, we introduce two measures of genetic relatedness and develop optimal estimators for them. One is genetic covariance, which is defined to be the inner product of the two regression vectors, and another is genetic correlation, which is a normalized inner product by their lengths. We…

误分类多变量区间删失数据的边际贝叶斯半参数建模AI 解读

Motivated by data gathered in an oral health study, we propose a Bayesian nonparametric approach for population-averaged modeling of correlated time-to-event data, when the responses can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of the event is subject to misclassification. The joint model for the true, unobserved time-to-event data is defined semiparametrically; proportional hazards,…

贝叶斯层级变稀疏回归及癌症蛋白基因组学应用AI 解读

Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein–gene…

多元凸回归及其变体的计算框架AI 解读

We study the nonparametric least squares estimator (LSE) of a multivariate convex regression function. The LSE, given as the solution to a quadratic program with O(n2) linear constraints (n being the sample size), is difficult to compute for large problems. Exploiting problem specific structure, we propose a scalable algorithmic framework based on the augmented Lagrangian method to compute the LSE. We develop a novel approach to obtain smooth convex approximations to the…

函数型图模型AI 解读

Graphical models have attracted increasing attention in recent years, especially in settings involving high-dimensional data. In particular, Gaussian graphical models are used to model the conditional dependence structure among multiple Gaussian random variables. As a result of its computational efficiency, the graphical lasso (glasso) has become one of the most popular approaches for fitting high-dimensional graphical models. In this paper, we extend the graphical models…

治疗选择下的存活遵从者效应：及时入住重症监护室的应用AI 解读

Pretreatment selection or censoring (“selection on treatment”) can occur when two treatment levels are compared ignoring the third option of neither treatment, in “censoring by death” settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such…

Group SLOPE：预测变量组的自适应选择AI 解读

Damian Brzyskiab*, Alexej Gossmannc, Weijie Sud & Małgorzata Bogdanea Department of Epidemiology and Biostatistics, Indiana University, Bloomington, INb Institute of Mathematics, Jagiellonian University, Cracow, Polandc Bioinnovation PhD Program, Tulane University, New Orleans, LAd Department of Statistics, University of Pennsylvania, Philadelphia, PAe Institute of Mathematics, University of Wroclaw, Wroclaw, PolandCONTACT Damian Brzyski dbrzyski@iu.edu Department of…

模型误设下的空间相依多重检验及极端气候归因应用AI 解读

The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a…

球面到球面回归的非参数旋转方法AI 解读

Marco Di Marzioa, Agnese Panzerab & Charles C. Taylorc*a DMQTE, Università di Chieti-Pescara, Pescara, Italyb DiSIA, Università di Firenze, Firenze, Italyc Department of Statistics, University of Leeds, Leeds, UKCONTACT Charles C. Taylor charles@maths.leeds.ac.uk Department of Statistics, University of Leeds, Leeds LS2 9JT, UKSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.ABSTRACTRegression of data represented as points…

多元时间序列的自适应贝叶斯时频分析AI 解读

This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood based penalized spline models of modified Cholesky components, which provide…

惩罚高斯随机场复杂度的先验构造AI 解读

Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent penalized complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional, and three-dimensional Matérn GRFs with fixed…

删失无偏回归树与集成学习AI 解读

This paper proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the CART and Random Forests algorithms for general loss functions, and in the latter case more general bootstrap procedures, are both introduced. These results, in combination with an extension of the theory of censoring unbiased transformations applicable to loss functions, underpin the development of two new classes of algorithms for…

小角 X 射线散射实验中回转半径的最小均方误差估计AI 解读

Small-angle X-ray scattering (SAXS) is a technique that yields low-resolution structural information of biological macromolecules by exposing a large ensemble of molecules in solution to a powerful X-ray beam. The beam interacts with the molecules and the intensity of the scattered beam is recorded on a detector plate. The radius of gyration for a molecule, which is a measure of the spread of its mass, can be estimated from the lowest scattering angles of SAXS data. This…

基于最大似然的线性非高斯成分分析AI 解读

Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis (PCA) is used for dimension reduction prior to ICA (PCA+ICA), which could remove important information. The problem is that interesting independent components (ICs) could be mixed in several principal components that are discarded and then these ICs cannot be recovered. We formulate a…

FSEM：双胞胎函数型数据的函数型结构方程模型AI 解读

The aim of this paper is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental…