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作者：Rosenbaum, Paul R.; Zubizarreta, Jose R.
作者单位：University of Pennsylvania; Harvard University; Harvard University
摘要：In experimental design, aliasing of effects occurs in fractional factorial experiments, where certain low order factorial effects are indistinguishable from certain high order interactions: low order contrast weights may be orthogonal to one another, while their higher order interactions are aliased and not identified. In observational studies, aliasing occurs when certain combinations of covariates-for example, time period and various eligibility criteria for treatment-perfectly predict the t...
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作者：Han, Dongxiao; Zheng, Siming; Shen, Guohao; Song, Xinyuan; Sun, Liuquan; Huang, Jian
作者单位：Nankai University; Nankai University; Chinese University of Hong Kong; Hong Kong Polytechnic University; Chinese Academy of Sciences; Hong Kong Polytechnic University
摘要：This article introduces a unified approach to estimating the mutual density ratio, defined as the ratio between the joint density function and the product of the individual marginal density functions of two random vectors. It serves as a fundamental measure for quantifying the relationship between two random vectors. Our method uses the Bregman divergence to construct the objective function and leverages deep neural networks to approximate the logarithm of the mutual density ratio. We establis...
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作者：Maojun, Sun; Han, Ruijian; Jiang, Binyan; Qi, Houduo; Sun, Defeng; Yuan, Yancheng; Huang, Jian
作者单位：Hong Kong Polytechnic University; Hong Kong Polytechnic University
摘要：We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large language models. LAMBDA is designed to address data analysis challenges in data-driven applications through innovatively designed data agents using natural language. At the core of LAMBDA are two key agent roles: the programmer and the inspector, which are engineered to work together seamlessly. Specifically, the programmer generates code based o...
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作者：Xu, Shirong; Sun, Will Wei; Cheng, Guang
作者单位：University of California System; University of California Los Angeles; Purdue University System; Purdue University
摘要：In various real-world scenarios, such as recommender systems and political surveys, pairwise rankings are commonly collected and used for rank aggregation to derive an overall ranking of items. However, preference rankings can reveal individuals' personal preferences, highlighting the need to protect them from exposure in downstream analysis. In this article, we address the challenge of preserving privacy while ensuring the utility of rank aggregation based on pairwise rankings generated from ...
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作者：Kim, Myungjin; Wang, Lily; Wang, Huixia Judy
作者单位：Kyungpook National University (KNU); George Mason University; George Washington University
摘要：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 uses bivariate pen...
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作者：Cheng, Cong; Ke, Yuan; Zhang, Wenyang
作者单位：University System of Georgia; University of Georgia; University of Macau
摘要：The estimation of large precision matrices is crucial in modern multivariate analysis. Traditional sparsity assumptions, while useful, often fall short of accurately capturing the dependencies among features. This article addresses this limitation by focusing on precision matrix estimation for multivariate data characterized by a flexible yet unknown group structure. We introduce a novel approach that begins with the detection of this unknown group structure, clustering features within the low...
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作者：Pearce, Michael; Erosheva, Elena A.
作者单位：Reed College - Portland; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
摘要：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 rankings and ratings-th...
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作者：Ye, Ting; Chen, Kan; Small, Dylan
作者单位：University of Washington; University of Washington Seattle; Harvard University; University of Pennsylvania
摘要：Does having firearms in the home increase suicide risk? To test this hypothesis, a matched case-control study can be performed, in which suicide case subjects are compared to living controls who are similar in observed covariates in terms of their retrospective exposure to firearms at home. In this application, cases can be defined using a broad case definition (suicide) or a narrow case definition (suicide occurred at home). The broad case definition offers a larger number of cases, but the n...
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作者：Rasines, Daniel Garcia; Young, Alastair
作者单位：Imperial College London
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作者：Zhou, Wenzhuo; Qu, Annie; Cooper, Keiland W.; Fortin, Norbert; Shahbaba, Babak
作者单位：University of California System; University of California Irvine; University of California System; University of California Irvine
摘要：Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework, which is able to effectively integrate information of various orders, extr...
