Journal of the American Statistical Association

最新文章

• 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...
• Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes AI翻译
  作者：Sun, Yilun; Wang, Lu
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：A dynamic treatment regime (DTR) is a sequence of decision rules that adapt to the time-varying states of an individual. Black-box learning methods have shown great potential in predicting the optimal treatments; however, the resulting DTRs lack interpretability, which is of paramount importance for medical experts to understand and implement. We present a stochastic tree-based reinforcement learning (ST-RL) method for estimating optimal DTRs in a multistage multitreatment setting with data fr...
• Distance-Based Analysis of Ordinal Data and Ordinal Time Series AI翻译
  作者：Weiss, Christian H.
  作者单位：Helmut Schmidt University
  摘要：The dissimilarity of ordinal categories can be expressed with a distance measure. A unified approach relying on expected distances is proposed to obtain well-interpretable measures of location, dispersion, or symmetry of random variables, as well as measures of serial dependence within a given process. For special types of distance, these analytic tools lead to known approaches for ordinal or real-valued random variables. We also analyze the sample counterparts of the proposed measures and der...
• Simple Local Polynomial Density Estimators AI翻译
  作者：Cattaneo, Matias D.; Jansson, Michael; Ma, Xinwei
  作者单位：Princeton University; CREATES; University of California System; University of California Berkeley; University of California System; University of California San Diego
  摘要：This article introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automatic, but does not require prebinning or any other transformation of the data. We study the main asymptotic properties of the estimator, and use these results to provide principled estimation, inference, and bandwidth selection methods. As a substantive application of our results, we develop a novel discontinuity in d...