Journal of the American Statistical Association

最新文章

• Smoothing Parameter and Model Selection for General Smooth Models Comment AI翻译
  作者：Greven, Sonja; Scheipl, Fabian
  作者单位：University of Munich
• Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence AI翻译
  作者：Zhao, Yize; Chung, Matthias; Johnson, Brent A.; Moreno, Carlos S.; Long, Qi
  作者单位：Cornell University; Virginia Polytechnic Institute & State University; University of Rochester; Emory University; Emory University
  摘要：Our work is motivated by a prostate cancer study aimed at identifying mRNA and miRNA biomarkers that are predictive of cancer recurrence after prostatectomy. It has been shown in the literature that incorporating known biological information on pathway memberships and interactions among biomarkers improves feature selection of high-dimensional biomarkers in relation to disease risk. Biological information is often represented by graphs or networks, in which biomarkers are represented by nodes ...
• Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times AI翻译
  作者：Xu, Yanxun; Muller, Peter; Wahed, Abdus S.; Thall, Peter F.
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Texas System; UTMD Anderson Cancer Center
  摘要：We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 x 2 factorial for frontline therapies only. Motivated, by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depend...
• Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes AI翻译
  作者：Zhou, Mingyuan; Padilla, Oscar Hernan Madrid; Scott, James G.
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
  摘要：We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns. The three distributions we consider are derived from the gamma-Poisson, gamma-negative binomial, and beta-negative binomial processes, which we refer to generically as a family of negative-binomial processes. Because the models lead to closed-form update equations within the context of a Gibbs sampler, they are natural candidates for nonparametric Bayesian priors o...
• Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources AI翻译
  作者：Chatterjee, Nilanjan; Chen, Yi-Hau; Maas, Paige; Carroll, Raymond J.
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; Academia Sinica - Taiwan; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：Information from various public and private data sources of extremely large sample sizes are now increasingly available for research purposes. Statistical methods are needed for using information from such big data sources while analyzing data from individual studies that may collect more detailed information required for addressing specific hypotheses of interest. In this article, we consider the problem of building regression models based on individual-level data from an internal study while...
• Smoothing Parameter and Model Selection for General Smooth Models AI翻译
  作者：Wood, Simon N.; Pya, Natalya; Saefken, Benjamin
  作者单位：University of Bristol; Nazarbayev University; KIMEP University; University of Gottingen; University of Gottingen
  摘要：This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tool...
• Bayesian Nonparametric Longitudinal Data Analysis AI翻译
  作者：Quintana, Fernando A.; Johnson, Wesley O.; Waetjen, L. Elaine; Gold, Ellen B.
  作者单位：Pontificia Universidad Catolica de Chile; University of California System; University of California Irvine; University of California System; University of California Davis; University of California System; University of California Davis
  摘要：Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observe...
• Efficient Sequential Monte Carlo With Multiple Proposals and Control Variates AI翻译
  作者：Li, Wentao; Chen, Rong; Tan, Zhiqiang
  作者单位：Lancaster University; Rutgers University System; Rutgers University New Brunswick
  摘要：Sequential Monte Carlo is a useful simulation-based method for online filtering of state-space models. For certain complex state-space models, a single proposal distribution is usually not satisfactory and using multiple proposal distributions is a general approach to address various aspects of the filtering problem. This article proposes an efficient method of using multiple proposals in combination with control variates. The likelihood approach of Tan (2004) is used in both resampling and es...
• Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering AI翻译
  作者：Coretto, Pietro; Hennig, Christian
  作者单位：University of Salerno; University of London; University College London
  摘要：The two main topics of this article are the introduction of the optimally tuned robust improper maximum likelihood estimator (OTRIMLE) for robust clustering based on the multivariate Gaussian model for clusters, and a comprehensive simulation study comparing the OTRIMLE to maximum likelihood in Gaussian mixtures with and without noise component, mixtures oft-distributions, and the TCLUST approach for trimmed clustering. The OTRIMLE uses an improper constant density for modeling outliers and no...
• Classification With Unstructured Predictors and an Application to Sentiment Analysis AI翻译
  作者：Wang, Junhui; Shen, Xiaotong; Sun, Yiwen; Qu, Annie
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; City University of Hong Kong; University of Minnesota System; University of Minnesota Twin Cities; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Unstructured data refer to information that lacks certain structures and cannot be organized in a predefined fashion. Unstructured data often involve words, texts, graphs, objects, or multimedia types of files that are difficult to process and analyze with traditional computational tools and statistical methods. This work explores ordinal classification for unstructured predictors with ordered class categories, where imprecise information concerning strengths of association between predictors ...