2018

最新文章

• A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data AI翻译
  作者：Zhou, Haiming; Hanson, Timothy
  作者单位：Northern Illinois University; University of South Carolina System; University of South Carolina Columbia; Medtronic
  摘要：A comprehensive, unified approach to modeling arbitrarily censored spatial survival data is presented for the three most commonly used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. Unlike many other approaches, all manner of censored survival times are simultaneously accommodated including uncensored, interval censored, current-status, left and right censored, and mixtures of these. Left-truncated data are also accommodated leading to models for ...
• Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide Comment AI翻译
  作者：Chevallier, Frederic; Breon, Francois-Marie
  作者单位：CEA; Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Universite Paris Cite
  摘要：Based on the measurements of the OCO-2 satellite, Noel Cressie addresses a particularly hard challenge for Earth observation, arguably an extreme case in remote sensing. He is one of the very few who has expertise in most of the processing chain and his article brilliantly discusses the diverse underlying statistical challenges. In this comment, we provide a complementary view of the topic to qualify its prospects as drawn by N. Cressie at the end of his article. We first summarize the motivat...
• Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach AI翻译
  作者：Liu, Dungang; Zhang, Heping
  作者单位：University System of Ohio; University of Cincinnati; Yale University
  摘要：Ordinal outcomes are common in scientific research and everyday practice, and we often rely on regression models to make inference. A long-standing problem with such regression analyses is the lack of effective diagnostic tools for validating model assumptions. The difficulty arises from the fact that an ordinal variable has discrete values that are labeled with, but not, numerical values. The values merely represent ordered categories. In this article, we propose a surrogate approach to defin...
• A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI AI翻译
  作者：Yu, Cheng-Han; Prado, Raquel; Ombao, Hernando; Rowe, Daniel
  作者单位：University of California System; University of California Santa Cruz; King Abdullah University of Science & Technology; Marquette University
  摘要：Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small signal-to-noise ratios (SNR...
• Assessing Time-Varying Causal Effect Moderation in Mobile Health AI翻译
  作者：Boruvka, Audrey; Almirall, Daniel; Witkiewitz, Katie; Murphy, Susan A.
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of New Mexico
  摘要：In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high-risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderatorsindividual characteristics, time-varying context, or past treatmen...
• Nonparametric Adjustment for Measurement Error in Time-to-Event Data: Application to Risk Prediction Models AI翻译
  作者：Braun, Danielle; Gorfine, Malka; Katki, Hormuzd A.; Ziogas, Argyrios; Parmigiani, Giovanni
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Tel Aviv University; Technion Israel Institute of Technology; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; University of California System; University of California Irvine
  摘要：Mismeasured time-to-event data used as a predictor in risk prediction models will lead to inaccurate predictions. This arises in the context of self-reported family history, a time-to-event predictor often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for this type of error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjust...
• Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series AI翻译
  作者：Chen, Jia; Li, Degui; Linton, Oliver; Lu, Zudi
  作者单位：University of York - UK; University of York - UK; University of Cambridge; University of Southampton; University of Southampton
  摘要：We propose two semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto-regressive lags. Our objective is to obtain more accurate estimates and forecasts of time series by using a large number of conditioning variables in a nonparametric way. In the first scheme, we introduce a kernel sure independence screening (KSIS) technique to screen out the regressors whose marginal regression...
• Minimax Optimal Procedures for Locally Private Estimation AI翻译
  作者：Duchi, John C.; Jordan, Michael I.; Wainwright, Martin J.
  作者单位：Stanford University; Stanford University; University of California System; University of California Berkeley; University of California System; University of California Berkeley
  摘要：Working under a model of privacy in which data remain private even from the statistician, we study the tradeoff between privacy guarantees and the risk of the resulting statistical estimators. We develop private versions of classical information-theoretical bounds, in particular those due to Le Cam, Fano, and Assouad. These inequalities allow for a precise characterization of statistical rates under local privacy constraints and the development of provably (minimax) optimal estimation procedur...
• Functional Feature Construction for Individualized Treatment Regimes AI翻译
  作者：Laber, Eric B.; Staicu, Ana-Maria
  作者单位：North Carolina State University
  摘要：Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information measured at multiple t...
• Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions AI翻译
  作者：Li, Alexander Hanbo; Bradic, Jelena
  作者单位：University of California System; University of California San Diego
  摘要：This article examines the role and the efficiency of nonconvex loss functions for binary classification problems. In particular, we investigate how to design adaptive and effective boosting algorithms that are robust to the presence of outliers in the data or to the presence of errors in the observed data labels. We demonstrate that nonconvex losses play an important role for prediction accuracy because of the diminishing gradient propertiesthe ability of the losses to efficiently adapt to the...