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作者：Gaffi, Francesco; Durante, Daniele; Lijoi, Antonio; Prunster, Igor
作者单位：Bocconi University; University System of Maryland; University of Maryland College Park
摘要：Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are observed among the same set of nodes (edge-colored networks) or where a single notion of connectivity is measured between nodes belonging to different pre-specified layers (node-colored networks). While progress has been made in statistical modeling of edge-colored networks, principled approaches that flexibly account for...
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作者：Ben-Michael, Eli; Greiner, D. James; Imai, Kosuke; Jiang, Zhichao
作者单位：Carnegie Mellon University; Carnegie Mellon University; Harvard University; Harvard University; Harvard University; Sun Yat Sen University
摘要：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 is to analyze data f...
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作者：Fan, Xinyan; Fang, Kuangnan; Lan, Wei; Tsai, Chih-Ling
作者单位：Renmin University of China; Renmin University of China; Xiamen University; Xiamen University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of California System; University of California Davis
摘要：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 regression coefficients ...
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作者：Wu, Peng; Luo, Shanshan; Geng, Zhi
作者单位：Beijing Technology & Business University
摘要：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 extend this analys...
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作者：Shin, Sooahn; Lim, Johan; Park, Jong Hee
作者单位：Harvard University; Seoul National University (SNU); Seoul National University (SNU)
摘要：Ideal point estimation methods in the social sciences lack a principled approach for identifying multidimensional ideal points. We present a novel method for estimating multidimensional ideal points based on l(1) distance. In the Bayesian framework, the use of l(1) distance transforms the invariance problem of infinite rotational turns into the signed perpendicular problem, yielding posterior estimates that contract around a small area. Our simulation shows that the proposed method successfull...
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作者：Zhong, Peng; Brunner, Manuela; Opitz, Thomas; Huser, Raphael
作者单位：University of New South Wales Sydney; Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; Swiss Federal Institute for Forest, Snow & Landscape Research; INRAE; King Abdullah University of Science & Technology
摘要：Extreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change may affect the spatial extent of precipitation extremes, whose investigation often directly relies on simulations of precipitation from climate models. Here, we use a different strategy to investigate how future changes in spatial extents of precipitation extremes differ across climate zones and seasons in two river...
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作者：Gradu, Paula; Zrnic, Tijana; Wang, Yixin; Jordan, Michael I.
作者单位：University of California System; University of California Berkeley; University of Michigan System; University of Michigan; University of California System; University of California Berkeley
摘要：Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to double dipping, invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical ...
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作者：Li, Runze; Li, Weiming; Wang, Qinwen
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Shanghai University of Finance & Economics; Fudan University
摘要：Tyler's M estimator, as a robust alternative to the sample covariance matrix, has been widely applied in robust statistics. However, classical theory on Tyler's M estimator is mainly developed in the low-dimensional regime for elliptical populations. It remains largely unknown when the parameter of dimension p grows proportionally to the sample size n for general populations. By using the eigenvalues of Tyler's M estimator, this article develops tests for the identity and equality of shape mat...
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作者：Zhou, Jie; Hao, Botao; Wen, Zheng; Zhang, Jingfei; Sun, Will Wei
作者单位：Amazon.com; Alphabet Inc.; DeepMind; Emory University; Purdue University System; Purdue University
摘要：Multi-dimensional online decision making plays a crucial role in many real applications such as online recommendation and digital marketing. In these problems, a decision at each time is a combination of choices from different types of entities. To solve it, we introduce stochastic low-rank tensor bandits, a class of bandits whose mean rewards can be represented as a low-rank tensor. We consider two settings, tensor bandits without context and tensor bandits with context. In the first setting,...
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作者：Sit, Tony
作者单位：Chinese University of Hong Kong
