The Annals of Applied Statistics

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• THE BAYESIAN NESTED LASSO FOR MIXED FREQUENCY REGRESSION MODELS AI翻译
  作者：Ghosh, Satyajit; Khare, Kshitij; Michailidis, George
  作者单位：US Food & Drug Administration (FDA); State University System of Florida; University of Florida; State University System of Florida; University of Florida
  摘要：Even though many time series are sampled at different frequencies, their joint evolution is usually modeled and analyzed at a common low frequency. The mixed data sampling (MIDAS) framework was developed to enable joint modeling of mixed frequency temporally evolving data with GDP forecasting as a key motivating application. In this paper we develop a fully Bayesian method to jointly estimate both the appropriate lag as well as the regression coefficients in linear models wherein the response ...
• BAYESIAN ADDITIVE REGRESSION TREES FOR GENOTYPE BY ENVIRONMENT INTERACTION MODELS AI翻译
  作者：Sarti, Danilo A.; Prado, Estevao B.; Inglis, Alan N.; Dos Santos, Antonia A. L.; Hurley, Catherine B.; Moral, Rafael A.; Parnell, Andrew C.
  作者单位：Maynooth University; Maynooth University
  摘要：We propose a new class of models for the estimation of genotype by environment (GxE) interactions in plant-based genetics. Our approach, named AMBARTI, uses semiparametric Bayesian additive regression trees to accurately capture marginal genotypic and environment effects along with their interaction in a cut Bayesian framework. We demonstrate that our approach is competitive or superior to similar models widely used in the literature via both simulation and a real world dataset. Furthermore, w...
• DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES AI翻译
  作者：Sun, Yifei; Chiou, Sy Han; Wu, Colin O.; McGarry, Meghan E.; Huang, Chiung-Yu
  作者单位：Columbia University; University of Texas System; University of Texas Dallas; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI); University of California System; University of California San Francisco; University of California System; University of California San Francisco
  摘要：With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction, using survival tree ensembles, where an updated prediction can be performed when new information becomes available. Compared to conventional landmark prediction with f...
• HANDLING CATEGORICAL FEATURES WITH MANY LEVELS USING A PRODUCT PARTITION MODEL AI翻译
  作者：Criscuolo, Tulio L.; Assuncao, Renato M.; Loschi, Rosangela H.; Meira Jr, Wagner; Cruz-Reyes, Danna
  作者单位：Universidade Federal de Minas Gerais; Environmental Systems Research Institute, Inc. (ESRI); Universidade Federal de Minas Gerais; National University of Rosario
  摘要：A common difficulty in data analysis is how to handle categorical predictors with a large number of levels or categories. Few proposals have been developed to tackle this important and frequent problem. We introduce a generative model that simultaneously carries out the model fitting and the aggregation of the categorical levels into larger groups. We represent the categorical predictor by a graph where the nodes are the categories and establish a probability distribution over meaningful parti...
• INDIVIDUALIZED RISK ASSESSMENT OF PREOPERATIVE OPIOID USE BY INTERPRETABLE NEURAL NETWORK REGRESSION AI翻译
  作者：Sun, Yuming; Kang, Jian; Brummett, Chad; Li, Yi
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Understanding the risk of preoperative opioid use helps establish patient-centered pain management. In the field of machine learning, deep neural network (DNN) has emerged as a powerful means for risk assessment because of its superb prediction power; however, the blackbox algorithms may make the re...
• A RELUCTANT ADDITIVE MODEL FRAMEWORK FOR INTERPRETABLE NONLINEAR INDIVIDUALIZED TREATMENT RULES AI翻译
  作者：Maronge, Jacob M.; Huling, Jared D.; Chen, Guanhua
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
  摘要：Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioners make sense of treatment decisions, yet there is a need for ITRs to be flexible to effectively model complex biomedical data for treat-ment decision making. Many ITR approaches either focus on linear ITRs, which may perform poorly when true optima...
• PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER AI翻译
  作者：Yao, Tsung-hung; Wu, Zhenke; Bharath, Karthik; Li, Jinju; Baladandayuthapani, Veerabhadran
  作者单位：University of Michigan System; University of Michigan; University of Nottingham
  摘要：Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems, such as patient-derived xenografts (PDX), have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper we propose a novel Bayesian probabilistic tree-based frame...
• ESTIMATING THE EFFECTS OF A CALIFORNIA GUN CONTROL PROGRAM WITH MULTITASK GAUSSIAN PROCESSES AI翻译
  作者：Ben-Michael, Eli; Arbour, David; Feller, Avi; Franks, Alexander; Raphael, Steven
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Adobe Systems Inc.; University of California System; University of California Berkeley; University of California System; University of California Santa Barbara; University of California System; University of California Berkeley
  摘要：Gun violence is a critical public safety concern in the United States. In 2006, California implemented a unique firearm monitoring program, the Armed and Prohibited Persons System (APPS), to address gun violence in the state. The APPS program first identifies those firearm owners who be-come prohibited from owning one, due to federal or state law, then confiscates their firearms. Our goal is to assess the effect of APPS on California murder rates using annual, state-level crime data across the...
• ASSOCIATION AND CAUSATION: ATTRIBUTES AND EFFECTS OF JUDGES IN EQUAL EMPLOYMENT OPPORTUNITY COMMISSION LITIGATION OUTCOMES AI翻译
  作者：Sobel, Michael E.; Wawro, Gregory J.; Farhang, Sean
  作者单位：Columbia University; Columbia University; University of California System; University of California Berkeley
  摘要：A large literature on judicial decision making asks if judges with different features of an attribute (e.g, sex, race) adjudicate cases differently. Researchers estimate models for case outcomes, interpreting coefficients associated with attributes as effects. But attributes are not treatments. While these coefficients indicate how judges with different features adjudicate the different cases they are assigned, ideally, different judges should be compared on a common set of cases. We construct...
• OPTIMAL SAMPLING DESIGNS FOR MULTIDIMENSIONAL STREAMING TIME SERIES WITH APPLICATION TO POWER GRID SENSOR DATA AI翻译
  作者：Xie, Rui; Bai, Shuyang; Ma, Ping
  作者单位：State University System of Florida; University of Central Florida; University System of Georgia; University of Georgia
  摘要：The Internet of Things (IoT) system generates massive high-speed temporally correlated streaming data and is often connected with online inference tasks under computational or energy constraints. Online analysis of these streaming time series data often faces a trade-off between statistical efficiency and computational cost. One important approach to balance this trade-off is sampling, where only a small portion of the sample is selected for the model fitting and update. Motivated by the deman...