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作者：Chen, Haoyu; Lu, Wenbin; Song, Rui
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作者：Forastiere, Laura; Airoldi, Edoardo M.; Mealli, Fabrizia
作者单位：Yale University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of Florence
摘要：Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of interference, for instance, potential outcomes of a unit depend on their treatment as well as on the treatments of other units, such as their neighbors in the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended-say, to include the treatment of neighbors, and...
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作者：Scealy, Janice L.; Wood, Andrew T. A.
作者单位：Australian National University
摘要：Robust estimation of location for data on the unit sphere is an important problem in directional statistics even though the influence functions of the sample mean direction and other location estimators are bounded. A significant limitation of previous literature on this topic is that robust estimators and procedures have been developed under the assumption that the underlying population is rotationally symmetric. This assumption often does not hold with real data and in these cases there is a...
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作者：Khim, Justin; Loh, Po-Ling
作者单位：Carnegie Mellon University; University of Wisconsin System; University of Wisconsin Madison; Columbia University
摘要：We formulate and analyze a novel hypothesis testing problem for inferring the edge structure of an infection graph. In our model, a disease spreads over a network via contagion or random infection, where the times between successive contagion events are independent exponential random variables with unknown rate parameters. A subset of nodes is also censored uniformly at random. Given the observed infection statuses of nodes in the network, the goal is to determine the underlying graph. We pres...
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作者：Zhou, Jincheng; Hodges, James S.; Chu, Haitao
作者单位：Amgen; University of Minnesota System; University of Minnesota Twin Cities
摘要：Noncompliance with assigned treatments is a common challenge in analyzing and interpreting randomized clinical trials (RCTs). One way to handle noncompliance is to estimate the complier-average causal effect (CACE), the intervention's efficacy in the subpopulation that complies with assigned treatment. In a two-step meta-analysis, one could first estimate CACE for each study, then combine them to estimate the population-averaged CACE. However, when some trials do not report noncompliance data,...
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作者：Qiu, Hongxiang; Carone, Marco; Sadikova, Ekaterina; Petukhova, Maria; Kessler, Ronald C.; Luedtke, Alex
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作者：Dai, Ben; Shen, Xiaotong; Wang, Junhui; Qu, Annie
作者单位：University of Minnesota System; University of Minnesota Twin Cities; City University of Hong Kong; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Personalized prediction presents an important yet challenging task, which predicts user-specific preferences on a large number of items given limited information. It is often modeled as certain recommender systems focusing on ordinal or continuous ratings, as in collaborative filtering and content-based filtering. In this article, we propose a new collaborative ranking system to predict most-preferred items for each user given search queries. Particularly, we propose a psi-ranker based on rank...
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作者：Hoffman, Kentaro; Hannig, Jan; Zhang, Kai
作者单位：University of North Carolina; University of North Carolina Chapel Hill
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作者：Paulon, Giorgio; Llanos, Fernando; Chandrasekaran, Bharath; Sarkar, Abhra
作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：Understanding how adult humans learn nonnative speech categories such as tone information has shed novel insights into the mechanisms underlying experience-dependent brain plasticity. Scientists have traditionally examined these questions using longitudinal learning experiments under a multi-category decision making paradigm. Drift-diffusion processes are popular in such contexts for their ability to mimic underlying neural mechanisms. Motivated by these problems, we develop a novel Bayesian s...
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作者：Han, Sukjin
作者单位：University of Bristol
