2020

最新文章

• Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications AI翻译
  作者：Chen, Yunxiao; Li, Xiaoou; Zhang, Siliang
  作者单位：University of London; London School Economics & Political Science; University of Minnesota System; University of Minnesota Twin Cities; Fudan University
• Theory of Spatial Statistics: A Concise Introduction AI翻译
  作者：Schoenberg, Frederic P.
  作者单位：University of California System; University of California Los Angeles
• From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation: Rejoinder AI翻译
  作者：Rosset, Saharon; Tibshirani, Ryan J.
  作者单位：Tel Aviv University; Carnegie Mellon University; Carnegie Mellon University
• Flexible Sensitivity Analysis for Observational Studies Without Observable Implications AI翻译
  作者：Franks, Alexander M.; D'Amour, Alexander; Feller, Avi
  作者单位：University of California System; University of California Santa Barbara; Alphabet Inc.; Google Incorporated; University of California System; University of California Berkeley
  摘要：A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity analysis approaches inadvertently impose restrictions that are at odds with modern causal inference methods, which emphasize flexible models for observed data. To address this issue, we propose a framework that allows (1) flexible models for the obse...
• Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning AI翻译
  作者：Luckett, Daniel J.; Laber, Eric B.; Kahkoska, Anna R.; Maahs, David M.; Mayer-Davis, Elizabeth; Kosorok, Michael R.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; Stanford University
  摘要：The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible healthcare for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decisio...
• Functional Horseshoe Priors for Subspace Shrinkage AI翻译
  作者：Shin, Minsuk; Bhattacharya, Anirban; Johnson, Valen E.
  作者单位：University of South Carolina System; University of South Carolina Columbia; Texas A&M University System; Texas A&M University College Station
  摘要：We introduce a new shrinkage prior on function spaces, called the functional horseshoe (fHS) prior, that encourages shrinkage toward parametric classes of functions. Unlike other shrinkage priors for parametric models, the fHS shrinkage acts on the shape of the function rather than inducing sparsity on model parameters. We study the efficacy of the proposed approach by showing an adaptive posterior concentration property on the function. We also demonstrate consistency of the model selection p...
• Nonparametric Estimation of Copula Regression Models With Discrete Outcomes AI翻译
  作者：Yang, Lu; Frees, Edward W.; Zhang, Zhengjun
  作者单位：University of Amsterdam; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：Multivariate discrete outcomes are common in a wide range of areas including insurance, finance, and biology. When the interplay between outcomes is significant, quantifying dependencies among interrelated variables is of great importance. Due to their ability to accommodate dependence flexibly, copulas are being applied increasingly. Yet, the application of copulas on discrete data is still in its infancy; one of the biggest barriers is the nonuniqueness of copulas, calling into question mode...
• IPAD: Stable Interpretable Forecasting with Knockoffs Inference AI翻译
  作者：Fan, Yingying; Lv, Jinchi; Sharifvaghefi, Mahrad; Uematsu, Yoshimasa
  作者单位：University of Southern California; University of Southern California; Tohoku University
  摘要：Interpretability and stability are two important features that are desired in many contemporary big data applications arising in statistics, economics, and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped. To this end, in this article, we exploit the general framework of model-X knockoffs in...
• Comment on A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression AI翻译
  作者：Fan, Jianqing; Ma, Cong; Wang, Kaizheng
  作者单位：Princeton University; University of California System; University of California Berkeley; Columbia University
• Learning When-to-Treat Policies AI翻译
  作者：Nie, Xinkun; Brunskill, Emma; Wager, Stefan
  作者单位：Stanford University; Stanford University
  摘要：Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an advantage doubly robust estimator for learning such dynamic treatment rules using observational data under the assumption of sequential ignorabi...