496期

最新文章

• Multi-Domain Sampling With Applications to Structural Inference of Bayesian Networks AI翻译
  作者：Zhou, Qing
  作者单位：University of California System; University of California Los Angeles
  摘要：When a posterior distribution has multiple modes, unconditional expectations, such as the posterior mean, may not offer informative summaries of the distribution. Motivated by this problem, we propose to decompose the sample space of a multimodal distribution into domains of attraction of local modes. Domain-based representations are defined to summarize the probability masses of and conditional expectations on domains of attraction, which are much more informative than the mean and other unco...
• Maximum Likelihood Estimations and EM Algorithms With Length-Biased Data AI翻译
  作者：Qin, Jing; Ning, Jing; Liu, Hao; Shen, Yu
  作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); University of Texas System; UTMD Anderson Cancer Center; Baylor College of Medicine
  摘要：Length-biased sampling has been well recognized in economics, industrial reliability, etiology applications, and epidemiological, genetic, and cancer screening studies. Length-biased right-censored data have a unique data structure different from traditional survival data. The nonparametric and semiparametric estimation and inference methods for traditional survival data are not directly applicable for length-biased right-censored data. We propose new expectation-maximization algorithms for es...
• Semiparametric Stochastic Modeling of the Rate Function in Longitudinal Studies AI翻译
  作者：Zhu, Bin; Taylor, Jeremy M. G.; Song, Peter X. -K.
  作者单位：Duke University; Duke University; University of Michigan System; University of Michigan
  摘要：In longitudinal biomedical studies, there is often interest in the rate functions, which describe the functional rates of change of biomarker profiles. This article proposes a semiparametric approach to model these functions as the realizations of stochastic processes defined by stochastic differential equations. These processes are dependent on the covariates of interest and vary around a specified parametric function. An efficient Markov chain Monte Carlo algorithm is developed for inference...
• Predicting Viral Infection From High-Dimensional Biomarker Trajectories AI翻译
  作者：Chen, Minhua; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S.; Lucas, Joseph; Dunson, David; Carin, Lawrence
  作者单位：Duke University; Duke University; Duke University; Duke University
  摘要：There is often interest in predicting an individual's latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dy...
• Semiparametric Bayesian Modeling of Income Volatility Heterogeneity AI翻译
  作者：Jensen, Shane T.; Shore, Stephen H.
  作者单位：University of Pennsylvania; University System of Georgia; Georgia State University
  摘要：Research on income risk typically treats its proxy-income volatility, the expected magnitude of income changes-as if it were unchanged for an individual over time, the same for everyone at a point in time, or both. In reality, income risk evolves over time, and some people face more of it than others. To model heterogeneity and dynamics in (unobserved) income volatility, we develop a novel semiparametric Bayesian stochastic volatility model. Our Markovian hierarchical Dirichlet process (MHDP) ...
• Geostatistical Model Averaging for Locally Calibrated Probabilistic Quantitative Precipitation Forecasting AI翻译
  作者：Kleiber, William; Raftery, Adrian E.; Gneiting, Tilmann
  作者单位：National Center Atmospheric Research (NCAR) - USA; University of Washington; University of Washington Seattle; Ruprecht Karls University Heidelberg
  摘要：Accurate weather benefit many key societal functions and activities, including agriculture, transportation, recreation, and basic human and infrastructural safety. Over the past two decades, ensembles of numerical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate probabilistic forecasts for future weather events. However, ensemble systems are uncalibrated and biased, and thus need to be statistically postproce...
• High-Dimensional ODEs Coupled With Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification AI翻译
  作者：Lu, Tao; Liang, Hua; Li, Hongzhe; Wu, Hulin
  作者单位：University of Rochester; University of Pennsylvania
  摘要：Gene regulation is a complicated process. The interaction of many genes and their products forms an intricate biological network. Identification of this dynamic network will help us understand the biological processes in a systematic way. However, the construction of a dynamic network is very challenging for a high-dimensional system. In this article we propose to use a set of ordinary differential equations (ODE), coupled with dimensional reduction by clustering and mixed-effects modeling tec...
• Elastic Net Regression Modeling With the Orthant Normal Prior AI翻译
  作者：Hans, Chris
  作者单位：University System of Ohio; Ohio State University
  摘要：The elastic net procedure is a form of regularized optimization for linear regression that provides a bridge between ridge regression and the lasso. The estimate that it produces can be viewed as a Bayesian posterior mode under a prior distribution implied by the form of the elastic net penalty. This article broadens the scope of the Bayesian connection by providing a complete characterization of a class of prior distributions that generate the elastic net estimate as the posterior mode. The r...
• A Likelihood Ratio Test Based Method for Signal Detection With Application to FDA's Drug Safety Data AI翻译
  作者：Huang, Lan; Zalkikar, Jyoti; Tiwari, Ram C.
  作者单位：US Food & Drug Administration (FDA); US Food & Drug Administration (FDA)
  摘要：Several statistical methods that are available in the literature to analyze postmarket safety databases, such as the U.S. Federal Drug Administration's (FDA) adverse event reporting system (AERS), for identifying drug-event combinations with disproportionately high frequencies, are subject to high false discovery rates. Here, we propose a likelihood ratio test (LRT) based method and show, via an extensive simulation study, that the proposed method while retaining good power and sensitivity for...
• Rasch Model and Its Extensions for Analysis of Aphasic Deficits in Syntactic Comprehension AI翻译
  作者：Gutman, Roee; DeDe, Gayle; Caplan, David; Liu, Jun S.
  作者单位：Brown University; University of Arizona; Harvard University; Harvard University Medical Affiliates; Massachusetts General Hospital; Harvard University
  摘要：Aphasia is the loss of the ability to produce and/or comprehend language, due to injury to brain areas responsible for these functions. Aphasic patients' performance on comprehension tests has traditionally been related both to the patient's individual ability and to the difficulty of the test questions. The natural choice for analysis of these test results is the Rasch model. It assumes that the probability of a patient responding correctly to a question is the inverse-logit function of the d...