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具有预言性质与几何收敛性的通信高效分布式稀疏学习AI 解读

This article introduces two highly efficient distributed non-convex sparse learning algorithms. Our approach accommodates non-convexity in both the loss function and penalty, acknowledging the potential non-uniqueness of local minimizers due to the inherent non-convexity. The development of an algorithm that ensures convergence to a locally minimal solution with desired statistical properties becomes imperative in this context. To overcome this challenge, we propose a…

纵向轨迹特征的动态回归AI 解读

Chronic disease studies often collect data on biological and clinical markers at follow-up visits to monitor disease progression. Viewing such longitudinal measurements governed by latent continuous trajectories, we develop a new dynamic regression framework to investigate the heterogeneity pattern of certain features of the latent individual trajectory that may carry substantive information on disease risk or status. Employing the strategy of multi-level modeling, we…

正例与未标记数据：模型、估计、推断与分类AI 解读

This study introduces a new approach to addressing the positive and unlabeled (PU) data through the double exponential tilting model (DETM) under a transfer learning framework. Traditional methods often fall short because they only apply to the common distributions (CD) PU data (also known as the selected completely at random PU data), where the labeled positive and unlabeled positive data are assumed to be from the same distribution. In contrast, our DETM’s dual structure…

核方法与筛方法结合：稀疏纵向协变量下的变换风险模型AI 解读

We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose combining kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator…

贝叶斯后验分析设计的低成本方法AI 解读

To design Bayesian studies, criteria for the operating characteristics of posterior analyses – such as power and the type I error rate – are often assessed by estimating sampling distributions of posterior probabilities via simulation. In this paper, we propose an economical method to determine optimal sample sizes and decision criteria for such studies. Using our theoretical results that model posterior probabilities as a function of the sample size, we assess operating…

利用低秩结构进行多维领域泛化AI 解读

In conventional statistical and machine learning methods, it is typically assumed that the test data are identically distributed with the training data. However, this assumption does not always hold, especially in applications where the target population are not well-represented in the training data. This is a notable issue in health-related studies, where specific ethnic populations may be underrepresented, posing a significant challenge for researchers aiming to make…

网络变系数模型AI 解读

We propose a novel network-varying coefficient model that extends traditional varying coefficient models to accommodate network data. The main idea is to model the regression coefficients as the functions of the latent “locations” of network nodes that drive formation of the network. To estimate the model, we identify the latent “locations” via the latent space model and then develop an iterative projected gradient descent algorithm by optimizing the network parameters and…

高维卷积秩回归的统计推断AI 解读

High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the rank loss brings great challenges to the computation. To solve this critical issue, high-dimensional convoluted rank regression has been recently proposed, introducing penalized convoluted rank regression estimators. However, these developed estimators cannot be directly used to make inference. In…

高维离散数据的程度异质性潜在类别分析AI 解读

The latent class model is a widely used mixture model for multivariate discrete data. Besides the existence of qualitatively heterogeneous latent classes, real data often exhibit additional quantitative heterogeneity nested within each latent class. The modern latent class analysis also faces extra challenges, including the high-dimensionality, sparsity, and heteroscedastic noise inherent in discrete data. Motivated by these phenomena, we introduce the Degree-heterogeneous…

从自动建模视角估计过参数化模型AI 解读

From a model-building perspective, we propose a paradigm shift for fitting over-parameterized models. Philosophically, the mindset is to fit models to future observations rather than to the observed sample. Technically, given an imputation method to generate future observations, we fit over-parameterized models to these future observations by optimizing an approximation of the desired expected loss function based on its sample counterpart and an adaptive duality function.…

高维线性假设检验的一种新方法AI 解读

This paper proposes an innovative double power-enhanced testing procedure for inference on high-dimensional linear hypotheses in high-dimensional regression models. Through a projection approach that aims to separate useful inferential information from the nuisance one, our proposed test accurately accounts for the impact of high-dimensional nuisance parameters. We discover that with a carefully-designed projection matrix, the projection procedure enables us to transform the…

大型向量自回归模型中的网络格兰杰因果关系发现AI 解读

This article proposes novel inferential procedures for discovering the network Granger causality in highdimensional vector autoregressive models.In particular, we mainly offer two multiple testing procedures designed to control the false discovery rate (FDR).The first procedure is based on the limiting normal distribution of the t-statistics with the debiased lasso estimator.The second procedure is its bootstrap version.We also provide a robustification of the first…

高维椭圆模型中 R² 统计量的自适应调整AI 解读

The 𝑅2 statistic and its classic adjusted version, say 𝑅*2, tend to overestimate the multiple correlation coefficient when dealing with multivariate data that exhibit heavy tails and tail dependence. This can result in an incorrect significance of correlation in high-dimensional scenarios. A new adaptive adjustment to the 𝑅2 statistic is proposed in this paper, which applies to a general population model that covers the family of elliptical distributions and an…

多项动态混合 Logit 模型中的统计推断AI 解读

In this paper we propose a general multinomial dynamic mixed logits model which explains how a multinomial/categorical response at a given time can be affected by (1) an individual’s categorical fixed covariates, (2) certain category prone random effects, and (3) an individual’s past multinomial responses. This model may be considered as a generalization of the (a) existing multinomial dynamic fixed models to the mixed model setup with category prone random effects; or (b)…

含吸收态的区间删失多状态数据半参数回归分析AI 解读

In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved. In this paper, we provide a general…

利用高维部分线性半参数混合效应模型比较纵向轨迹AI 解读

In longitudinal research, it is essential to compare sets of trajectories, commonly seen as changes over time in different treatment or patient groups. This paper presents a partial linear semiparametric mixed-effects model (PLSMM) for the analysis and comparison of nonlinear longitudinal trajectories with high-dimensional covariates across groups. Our flexible modeling framework can effectively handle complex temporal effects and extensive data while providing statistical…

再生核希尔伯特空间上的非参数期望短缺回归估计与推断AI 解读

Expected shortfall (ES) has emerged as an important metric for characterizing the tail behavior of a random outcome, specifically associated with rarer events that entail severe consequences. In climate science, the threats of flooding and heatwaves loom large, impacting natural environments and human communities. In actuarial studies, a key observation in modeling insurance claim sizes is that features exhibit distinct effects in explaining small and large claims. This…

含随机变点的区间删失失效时间数据估计与变量选择AI 解读

Motivated by a breast cancer study, we consider regression analysis of interval-censored failure time data in the presence of a random change point. Although a great deal of literature on interval-censored data has been established, there does not seem to exist an established method that can allow for the existence of random change points. Such data can occur in, for example, clinical trials where the risk of a disease may dramatically change when some biological indexes of…

稀疏异质性下的最优多任务线性回归与情境老虎机AI 解读

Large and complex datasets are often collected from several, possibly heterogeneous sources. Multitask learning methods improve efficiency by leveraging commonalities across datasets while accounting for possible differences among them. Here, we study multitask linear regression and contextual bandits under sparse heterogeneity, where the source/task-associated parameters are equal to a global parameter plus a sparse task-specific term. We propose a novel two-stage estimator…

通过数据组合估计平均处理效应的比较分析AI 解读

There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive comparative analyses. In this article, we focus on a typical scenario involving a source dataset and a target dataset. We first design six settings under covariate shift and conduct a comparative analysis by deriving the semiparametric efficiency bounds for the ATE in the target population. We then…

利用广义变系数模型检测不规则区域中的局部信号AI 解读

In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate…

超高维协方差矩阵的两样本检验AI 解读

In this article, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the sizes of both of the two samples.Our proposed methodology relies on a data splitting procedure and a comparison of a set of well selected eigenvalues of the sample covariance matrices on the split datasets.Compared to the existing methods, our methodology is adaptive in the sense that (i). it…

多元函数型线性回归中的系数形状对齐AI 解读

In multivariate functional data analysis, different functional covariates often exhibit homogeneity. The covariates with pronounced homogeneity can be analyzed jointly within the same group, offering a parsimonious approach to modeling multivariate functional data. In this article, a novel grouped multiple functional regression model with a new regularization approach termed “coefficient shape alignment” is developed to tackle functional covariates homogeneity. The modeling…

答复讨论者AI 解读

We thank the discussants for their thoughtful and stimulating comments. The goal of our paper was to highlight a robustness wedge: vector autoregressions (VARs) and linear local projections onto ob...