Journal of the American Statistical Association

最新文章

• Testing the Predictability of US Housing Price Index Returns Based on an IVX-AR Model AI翻译
  作者：Yang, Bingduo; Long, Wei; Peng, Liang; Cai, Zongwu
  作者单位：Sun Yat Sen University; Tulane University; University System of Georgia; Georgia State University; University of Kansas
  摘要：We use ten common macroeconomic variables to test for the predictability of the quarterly growth rate of house price index (HPI) in the United States during 1975:Q1-2018:Q2. We extend the instrumental variable based Wald statistic (IVX-KMS) proposed by Kostakis, Magdalinos, and Stamatogiannis to a new instrumental variable based Wald statistic (IVX-AR) which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model. Simulation results s...
• The Book of Why: The New Science of Cause and Effect. AI翻译
  作者：Aronow, Peter M.; Savje, Fredrik
  作者单位：Yale University
• Prediction and Inference With Missing Data in Patient Alert Systems AI翻译
  作者：Storlie, Curtis B.; Therneau, Terry M.; Carter, Rickey E.; Chia, Nicholas; Bergquist, John R.; Huddleston, Jeanne M.; Romero-Brufau, Santiago
  作者单位：Mayo Clinic
  摘要：We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-intensive care unit patients using similar to 100 variables (vitals, lab results, assessments, etc.). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the uncertai...
• Individualized Multilayer Tensor Learning With an Application in Imaging Analysis AI翻译
  作者：Tang, Xiwei; Bi, Xuan; Qu, Annie
  作者单位：University of Virginia; University of Minnesota System; University of Minnesota Twin Cities; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction models assuming a homogeneous feature structure. We develop an innovative multilayer tensor learning method to incorporate heterogeneity to a higher-order tensor decomposition and predict d...
• Copula Link-Based Additive Models for Right-Censored Event Time Data AI翻译
  作者：Marra, Giampiero; Radice, Rosalba
  作者单位：University of London; University College London; City St Georges, University of London; University of London
  摘要：This article proposes an approach to estimate and make inference on the parameters of copula link-based survival models. The methodology allows for the margins to be specified using flexible parametric formulations for time-to-event data, the baseline survival functions to be modeled using monotonic splines, and each parameter of the assumed joint survival distribution to depend on an additive predictor incorporating several types of covariate effects. All the model's coefficients as well as t...
• Estimation of Conditional Prevalence From Group Testing Data With Missing Covariates AI翻译
  作者：Delaigle, Aurore; Huang, Wei; Lei, Shaoke
  作者单位：University of Melbourne; University of Melbourne; Royal Children's Hospital Melbourne; Murdoch Children's Research Institute; Royal Children's Hospital Melbourne
  摘要：We consider estimating the conditional prevalence of a disease from data pooled according to the group testing mechanism. Consistent estimators have been proposed in the literature, but they rely on the data being available for all individuals. In infectious disease studies where group testing is frequently applied, the covariate is often missing for some individuals. There, unless the missing mechanism occurs completely at random, applying the existing techniques to the complete cases without...
• Multi-Armed Angle-Based Direct Learning for Estimating Optimal Individualized Treatment Rules With Various Outcomes AI翻译
  作者：Qi, Zhengling; Liu, Dacheng; Fu, Haoda; Liu, Yufeng
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Boehringer Ingelheim; Eli Lilly; Lilly Research Laboratories; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Estimating an optimal individualized treatment rule (ITR) based on patients' information is an important problem in precision medicine. An optimal ITR is a decision function that optimizes patients' expected clinical outcomes. Many existing methods in the literature are designed for binary treatment settings with the interest of a continuous outcome. Much less work has been done on estimating optimal ITRs in multiple treatment settings with good interpretations. In this article, we propose ang...
• Discussion of A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression AI翻译
  作者：Li, Xiudi; Shojaie, Ali
  作者单位：University of Washington; University of Washington Seattle
• High-Dimensional Precision Medicine From Patient-Derived Xenografts AI翻译
  作者：Rashid, Naim U.; Luckett, Daniel J.; Chen, Jingxiang; Lawson, Michael T.; Wang, Longshaokan; Zhang, Yunshu; Laber, Eric B.; Liu, Yufeng; Yeh, Jen Jen; Zeng, Donglin; Kosorok, Michael R.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University
  摘要：The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit t...
• Bayesian Model Search for Nonstationary Periodic Time Series AI翻译
  作者：Hadj-Amar, Beniamino; Rand, Barbel Finkenstadt; Fiecas, Mark; Levi, Francis; Huckstepp, Robert
  作者单位：University of Warwick; University of Minnesota System; University of Minnesota Twin Cities; University of Warwick; University of Warwick
  摘要：We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behavior. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo algorithm that simultaneously updates the change-points and the periodici...