510期

最新文章

• Index Models for Sparsely Sampled Functional Data AI翻译
  作者：Radchenko, Peter; Qiao, Xinghao; James, Gareth M.
  作者单位：University of Southern California
  摘要：The regression problem involving functional predictors has many important applications and a number of functional regression methods have been developed. However, a common complication in functional data analysis is one of sparsely observed curves, that is predictors that are observed, with error, on a small subset of the possible time points. Such sparsely observed data induce an errors-in-variables model, where one must account for measurement error in the functional predictors. Faced with s...
• Foundations for Envelope Models and Methods AI翻译
  作者：Cook, R. Dennis; Zhang, Xin
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; Florida State University
  摘要：Envelopes were recently proposed by Cook, Li and Chiaromonte as a method for reducing estimative and predictive variations in multivariate linear regression. We extend their formulation, proposing a general definition of an envelope and a general framework for adapting envelope methods to any estimation procedure. We apply the new envelope methods to weighted least squares, generalized linear models and Cox regression. Simulations and illustrative data analysis show the potential for envelope ...
• MAD Bayes for Tumor Heterogeneity-Feature Allocation With Exponential Family Sampling AI翻译
  作者：Xu, Yanxun; Mueller, Peter; Yuan, Yuan; Gulukota, Kamalakar; Ji, Yuan
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Baylor College of Medicine; NorthShore University Health System; University of Chicago
  摘要：We propose small-variance asymptotic approximations for inference on tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open research problem in biology. The lack of appropriate statistical inference is a critical gap in existing methods that the proposed approach aims to fill. We build on a hierarchical model with an exponential family likelihood and a feature allocation prior. The proposed implementation of posterior inference generalizes sim...
• Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity AI翻译
  作者：Lee, Juhee; Thall, Peter F.; Ji, Yuan; Mueller, Peter
  作者单位：University System of Ohio; Ohio State University; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; University of Texas Austin
  摘要：This article proposes a phase I/II clinical trial design for adaptively and dynamically optimizing each patient's dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, simila...
• Detection of Multiple Structural Breaks in Multivariate Time Series AI翻译
  作者：Preuss, Philip; Puchstein, Ruprecht; Dette, Holger
  作者单位：Ruhr University Bochum
  摘要：We propose a new nonparametric procedure (referred to as MuBreD) for the detection and estimation of multiple structural breaks in the autocovariance function of a multivariate (second-order) piecewise stationary process, which also identifies the components of the series where the breaks occur. MuBreD is based on a comparison of the estimated spectral distribution on different segments of the observed time series and consists of three steps: it starts with a consistent test, which allows us t...
• Post-Fisherian Experimentation: From Physical to Virtual AI翻译
  作者：Wu, C. F. Jeff
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Fisher's pioneering work in design of experiments has inspired further work with broader applications, especially in industrial experimentation. This article discusses three topics in physical experiments: principles of effect hierarchy, sparsity, and heredity for factorial designs, a new method called conditional main effect (CME) for de-aliasing aliased effects, and robust parameter design. I also review the recent emergence of virtual experiments on a computer. Some major challenges in comp...
• False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex AI翻译
  作者：Scott, James G.; Kelly, Ryan C.; Smith, Matthew A.; Zhou, Pengcheng; Kass, Robert E.
  作者单位：University of Texas System; University of Texas Austin; Alphabet Inc.; Google Incorporated; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University
  摘要：This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between in...
• Joint Bayesian Modeling of Binomial and Rank Data for Primate Cognition AI翻译
  作者：Barney, Bradley J.; Amici, Federica; Aureli, Filippo; Call, Josep; Johnson, Valen E.
  作者单位：University System of Georgia; Kennesaw State University; Max Planck Society; Universidad Veracruzana; Liverpool John Moores University; University of St Andrews; Max Planck Society
  摘要：In recent years, substantial effort has been devoted to methods for analyzing data containing mixed response types, but such techniques typically do not include rank data among the response types. Some unique challenges exist in analyzing rank data, particularly when ties are prevalent. We present techniques for jointly modeling binomial and rank data using Bayesian latent variable models. We apply these techniques to compare the cognitive abilities of nonhuman primates based on their performa...
• Semiparametric Accelerated Failure Time Modeling for Clustered Failure Times From Stratified Sampling AI翻译
  作者：Chiou, Sy Han; Kang, Sangwook; Yan, Jun
  作者单位：University of Minnesota System; University of Minnesota Duluth; Yonsei University; University of Connecticut; University of Connecticut; University of Connecticut
  摘要：Clustered failure times often arise from studies with stratified sampling designs where it is desired to reduce both cost and sampling error. Semiparametric accelerated failure time (AFT) models have not been used as frequently as Cox relative risk models in such settings due to lack of efficient and reliable computing routines for inferences. The challenge roots in the nonsmoothness of the rank-based estimating functions, and for clustered data, the asymptotic properties of the estimator from...
• New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes AI翻译
  作者：Zhao, Ying-Qi; Zeng, Donglin; Laber, Eric B.; Kosorok, Michael R.
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long-term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized trea...