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通过联合正则化深度神经网络估计单细胞异质基因网络AI 解读

Estimation of intracellular gene networks has been a critical component of single-cell transcriptomic data analysis, which can provide crucial insights into the complex interplay between genes, facilitating the discovery of the biological basis of human life at single-cell resolution. Despite notable achievements, existing methodologies often falter in their practicality, primarily due to their narrow focus on simplistic linear relationships and inadequate handling of…

用于导数建模的最优代入式高斯过程AI 解读

Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy…

统计研究的新兴知识趋势：协变量辅助动态主题模型分析AI 解读

Exploring the emerging knowledge trends within a particular discipline is of great interest to scientific researchers. It helps researchers to understand the historical development of their disciplines and to guide their future research directions. In this work, we focus on the fast-developing discipline, statistics, to investigate its knowledge trend and emerging topics in statistical research. To this end, we collect publications in top-tier statistical journals and…

因子增广矩阵回归AI 解读

We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, using techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm…

用于线性与非线性充分降维的带瓶颈层集成神经网络AI 解读

We introduce a unified, flexible, and easy-to-implement framework of sufficient dimension reduction that can accommodate both linear and nonlinear dimension reduction, and both the conditional distribution and the conditional mean as the targets of estimation. This unified framework is achieved by a specially structured neural network—the Belted and Ensembled Neural Network (BENN)—that consists of a narrow latent layer, which we call the belt, and a family of transformations…

人机交互的收益：混杂环境中的超级策略学习AI 解读

As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy…

基于汇总数据、消除赢家诅咒的稳健孟德尔随机化AI 解读

In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses using summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropy…

工作稳定性对意大利货币贫困的影响：因果小区域估计AI 解读

Job stability – encompassing secure contracts, adequate wages, social benefits, and career opportunities – is a critical determinant in reducing monetary poverty, as it provides households with reliable income and enhances economic well-being. This study draws on EU-SILC survey and census data to estimate the causal effect of job stability on monetary poverty across Italian provinces, quantifying its influence, and analyzing regional disparities. We introduce a novel causal…

局部平稳过程的贝叶斯非参数谱分析AI 解读

Stationarity plays a pivotal role in time series analysis. It is not only the basis for the derivation of general asymptotic theory but it also allows an efficient analysis in the frequency domain via the Whittle likelihood, based on the asymptotic independence of the Fourier coefficients. However, many regularly sampled data derived from the observation of physical or ecological processes, for instance, are only locally stationary. They exhibit slowly evolving spectra and…

高维条件因子模型估计的统一框架AI 解读

This article presents a general framework for estimating high-dimensional conditional latent factor models via constrained nuclear norm regularization. We establish large sample properties of the estimators and provide efficient algorithms for their computation. To improve practical applicability, we propose a cross-validation procedure for selecting the regularization parameter. Our framework unifies the estimation of various conditional factor models, enabling the…

基于条件尖峰—厚尾先验的协变量细化稳健部分信息迁移AI 解读

The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this paper, we propose a novel Bayesian transfer learning method named “CONCERT” to allow robust partial information transfer for high-dimensional data…

Riesz 表示元的对抗估计AI 解读

Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel…

基于 M 分位数的小区域估计量的偏差控制AI 解读

Projective outlier-robust M-quantile-based small area estimators can be substantially biased when the sample data contain representative outliers. In this article we propose two new predictive type bias corrected versions of these estimators for continuous and discrete outcomes. Given both area level and individual level outliers in the population, these new estimators are more efficient than the robust-predictive and robust-projective estimators that have been proposed in…

因子调整的模型平均AI 解读

We propose a model averaging method for high-dimensional regression with highly correlated covariates. We use a factor structure to model the covariate dependence, allowing the covariates to be decomposed into two uncorrelated or weakly correlated latent components: common factors and idiosyncratic components. The number of common factors is allowed to diverge. We average estimators from factor-adjusted candidate models with augmented predictors composed of estimated common…

Rasch 模型题目参数估计的随机配对极大似然方法AI 解读

The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals’ latent traits and their binary responses to assessments or questionnaires. In this article, we introduce a new likelihood-based estimator—random pairing maximum likelihood estimator (RP‐MLE) and its bootstrapped variant multiple random pairing MLE (MRP‐MLE) which faithfully estimate the item parameters in the Rasch model. The new…

函数型偏最小二乘：自适应估计与推断AI 解读

We study the linear regression model with a scalar response and a functional predictor, a canonical example of an ill-posed inverse problem. We show that the functional partial least-squares (PLS) estimator achieves convergence rates that are nearly minimax-optimal over a class of ellipsoids and propose an adaptive early-stopping procedure for selecting the number of PLS components. In addition, we develop a new test that detects parametric local alternatives. The test can…

模块化与半模块化贝叶斯推断的后验风险AI 解读

Modular Bayesian methods perform inference in models that are specified through a collection of coupled sub-models, known as modules. These modules often arise from modeling different data sources or from combining domain knowledge from different disciplines. “Cutting feedback” is a Bayesian inference method that ensures misspecification of one module does not affect inferences for parameters in other modules, and produces what is known as the cut posterior. However,…

因子数量的频带估计AI 解读

We introduce consistent estimators for the number of shocks driving large-dimensional dynamic factor models. Our estimator can be applied to single frequencies and specific frequency bands, making it suitable for disentangling shocks affecting dynamic models with a factor model representation. Noticeably, our estimator requires the time-series and cross-section size to diverge simultaneously without any constraint, and it is free of nuisance parameters, such as penalization…

借助辅助总体工具变量识别与多重稳健估计因果效应AI 解读

Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach,…

种群规模依赖分枝过程的一致最小二乘估计AI 解读

We derive the first conditionally consistent estimators for a class of parametric Markov population models with logistic growth, which are suitable for modelling endangered populations in restricted habitats with a carrying capacity. We focus on discrete-time parametric population-size-dependent branching processes, for which we propose a new class of weighted least-squares estimators based on a single trajectory of population size counts. We establish the consistency and…

SPARCC：右删失协变量模型中的半参数稳健估计AI 解读

In Huntington disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, achieving this goal entails modeling symptom severity as a function of the covariate 'time of diagnosis,' which is often heavily right-censored in observational studies. Existing estimators that handle right-censored covariates, such as the complete case estimator and maximum likelihood estimator, vary in their statistical efficiency and…

存在跨簇干扰的整群随机试验AI 解读

The literature on cluster-randomized trials typically allows for interference within but not across clusters. This may be implausible when units are irregularly distributed across space without well-separated communities, as clusters in such cases may not align with significant geographic, social, or economic divisions. This paper develops methods for reducing bias due to cross-cluster interference. We first propose an estimation strategy that excludes units not surrounded…

加料顺序实验的最优运行次序AI 解读

Order-of-addition experiments have emerged as a cornerstone in modern experimental design, yet the critical role of run-order has been entirely neglected in the literature. This oversight is surprising, given that the run order can significantly influence the cost, efficiency, and validity of the experiment. Certain run orders are inherently more economical and effective, while suboptimal orders may introduce unnecessary complexities or compromise results. To ensure…

基于多样化投影的含潜在因子高维空间自回归AI 解读

We study one particular type of multivariate spatial autoregression (MSAR) model with diverging dimensions in both responses and covariates. This makes the usual MSAR models no longer applicable due to the high computational cost. To address this issue, we propose a factor-augmented spatial autoregression (FSAR) model. FSAR is a special case of MSAR but with a novel factor structure imposed on the high-dimensional random error vector. The latent factors of FSAR are assumed…