论文检索

多层度修正随机块模型中的联合谱聚类AI 解读

Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating…

网络辅助回归的保形预测AI 解读

An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics. While standard regression methods that make use of both types of covariates may be used for prediction, statistical inference is complicated by the fact that the nodal summary statistics are often dependent in complex ways. We show that under…

块模型家族的网络拟合优度检验AI 解读

The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach, and we further extend it to…

我们遗漏了谁？刻画代表不足人群的原则性方法AI 解读

Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of…

用贝叶斯决策树集成估计异质性因果中介效应AI 解读

The causal inference literature has increasingly recognized that targeting treatment effect heterogeneity can lead to improved scientific understanding and policy recommendations. Similarly, studying the causal pathway connecting the treatment to the outcome can be useful. We address these problems in the context of causal mediation analysis . We introduce a varying coefficient model based on Bayesian additive regression trees to estimate and regularize heterogeneous causal…

通过外推进行安全政策学习：审前风险评估应用AI 解读

Algorithmic recommendations and decisions have become ubiquitous in today’s society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal…

复杂区域上分位数空间变系数模型的估计与推断AI 解读

This article presents a flexible quantile spatially varying coefficient model (QSVCM) for the regression analysis of spatial data. The proposed model enables researchers to assess the dependence of conditional quantiles of the response variable on covariates while accounting for spatial nonstationarity. Our approach facilitates learning and interpreting heterogeneity in spatial data distributed over complex or irregular domains. We introduce a quantile regression method that…

重复观测与纵向随机对象的测地线混合效应模型AI 解读

Mixed effect modeling for longitudinal data is challenging when the observed data are random objects, which are complex data taking values in a general metric space without either global linear or local linear (Riemannian) structure. In such settings the classical additive error model and distributional assumptions are unattainable. Due to the rapid advancement of technology, longitudinal data containing complex random objects, such as covariance matrices, data on Riemannian…

基于分位数函数的分布型结局回归及心率与活动量应用AI 解读

Modern clinical and epidemiological studies widely employ wearables to record parallel streams of real-time data on human physiology and behavior. With recent advances in distributional data analysis, these high-frequency data are now often treated as distributional observations resulting in novel regression settings. Motivated by these modeling setups, we develop a distributional outcome regression via quantile functions (DORQF) that expands existing literature with three…

通过自适应合并实现纵向网络的高效估计AI 解读

Longitudinal networks consist of sequences of temporal edges among multiple nodes, where the temporal edges are observed in real-time. They have become ubiquitous with the rise of online social platforms and e-commerce, but largely under-investigated in the literature. In this article, we propose an efficient estimation framework for longitudinal networks, leveraging strengths of adaptive network merging, tensor decomposition, and point processes. It merges neighboring…

高维期望短缺回归AI 解读

Expected shortfall is defined as the average over the tail below (or above) a certain quantile of a probability distribution. Expected shortfall regression provides powerful tools for learning the relationship between a response variable and a set of covariates while exploring the heterogeneous effects of the covariates. In the health disparity research, for example, the lower/upper tail of the conditional distribution of a health-related outcome, given high-dimensional…

目标处理效应的联邦自适应因果估计AI 解读

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level…

联邦元学习的稳健推断AI 解读

Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints due to privacy concerns. In this paper, we consider a general robust inference framework for federated meta-learning of data from multiple sites, enabling statistical inference for the prevailing model, defined as the one matching the majority of the…

匹配观察研究中隐蔽偏差分位数的敏感性分析AI 解读

Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern…

偏好建模：排名与评分的贝叶斯有限混合之混合模型AI 解读

Rankings and ratings are commonly used to express preferences but provide distinct and complementary information.Rankings give ordinal and scale-free comparisons but lack granularity; ratings provide cardinal and granular assessments but may be highly subjective or inconsistent.Collecting and analyzing rankings and ratings jointly has not been performed until recently due to a lack of principled methods.In this work, we propose a flexible, joint statistical model for…

偏分位数张量回归AI 解读

Tensors, characterized as multidimensional arrays, are frequently encountered in modern scientific studies. Quantile regression has the unique capacity to explore how a tensor covariate influences different segments of the response distribution. In this work, we propose a partial quantile tensor regression (PQTR) framework, which novelly applies the core principle of the partial least squares technique to achieve effective dimension reduction for quantile regression with a…

含因子面板模型中工具变量估计量的组合AI 解读

We address the estimation of factor-augmented panel data models using observed measurements to proxy for unobserved factors or loadings and explore the use of internal instruments to address the resulting endogeneity.The main challenge consists in that economic theory rarely provides insights into which measurements to choose as proxies when several are available.To overcome this problem, we propose a new class of estimators that are linear combinations of instrumental…

网络的分组稀疏 β 模型AI 解读

Sparsity, homogeneity, and heterogeneity are three important characteristics of many real-life networks. The recently proposed Sparse β-Model divides nodes into core ones and peripheral ones to accommodate sparsity, but the parameters of core nodes are assumed to be of similar magnitude, which may not be in line with applications. In this article, we propose the Group Sparse β-Model that splits the core nodes into groups and assumes different orders of magnitude of…

利用工具变量校正二元解释变量的误分类AI 解读

Estimators that exploit an instrumental variable to correct for misclassification in a binary regressor typically assume that the misclassification rates are invariant across all values of the instrument.We show this assumption is invalid in routine empirical settings.We derive a new estimator which allows misclassification rates to vary across values of the instrumental variable.Our key identifying assumption, that the sum of misclassification rates remains constant across…

配对实验中不完全依从下的推断AI 解读

This article studies inference for the local average treatment effect in randomized controlled trials with imperfect compliance where treatment status is determined according to “matched pairs.” By “matched pairs,” we mean that units are sampled iid from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Under weak assumptions governing the quality of the pairings, we…

重新审视含滞后因变量的面板二元选择模型AI 解读

This paper revisits the identification and estimation of a class of semiparametric (distribution-free) panel data binary choice models with lagged dependent variables, exogenous covariates, and entity fixed effects. We provide a novel identification strategy, using an “identification at infinity” argument. In contrast with the celebrated Honoré and Kyriazidou (2000), our method permits time trends of any form and does not suffer from the “curse of dimensionality”. We propose…

内生折点门槛回归AI 解读

This article considers an endogenous kink threshold regression model with an unknown threshold value in a time series as well as a panel data framework, where both the threshold variable and regressors are allowed to be endogenous. We construct our estimators from a nonparametric control function approach and derive the consistency and asymptotic distribution of our proposed estimators. Monte Carlo simulations are used to assess the finite sample performance of our proposed…

区组随机实验与再随机化实验中 Lasso 调整的设计型理论AI 解读

Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regression adjustment method based on the least absolute shrinkage and selection operator (Lasso) to efficiently estimate the average treatment effect in randomized block experiments with high-dimensional covariates. We derive the asymptotic properties of the proposed estimator and outline the conditions…

利用大规模在线产品评分网络进行客户定位的模型式协同聚类AI 解读

Given the widely available online customer ratings on products, the individual-level rating prediction and clustering of customers and products are increasingly important for sellers to create targeting strategies for expanding the customer base and improving product ratings.However, the massive missing data problem is a significant challenge for modeling online product ratings.To address this issue, we propose a new co-clustering methodology based on a bipartite network…