486期

最新文章

• Joint Models for the Association of Longitudinal Binary and Continuous Processes With Application to a Smoking Cessation Trial AI翻译
  作者：Liu, Xuefeng; Daniels, Michael J.; Marcus, Bess
  作者单位：East Tennessee State University; State University System of Florida; University of Florida; Brown University
  摘要：Joint models for the association of a logitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parametrized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for sampling from the posteri...
• A Class of Transformed Mean Residual Life Models With Censored Survival Data AI翻译
  作者：Sun, Liuquan; Zhang, Zhigang
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Memorial Sloan Kettering Cancer Center
  摘要：The mean residual life function is an attractive alternative to the survival function or the hazard function of a survival time in practice. It provides the remaining life expectancy of a subject surviving Lip to time t. In this study. We propose a class of transformed mean residual life models for fitting survival data under right censoring. To estimate the model parameters. we make use of the inverse probability of censoring weighting approach and develop a system of estimating equations. Ef...
• Partial Correlation Estimation by Joint Sparse Regression Models AI翻译
  作者：Peng, Jie; Wang, Pei; Zhou, Nengfeng; Zhu, Ji
  作者单位：University of California System; University of California Davis; Fred Hutchinson Cancer Center; University of Michigan System; University of Michigan
  摘要：In this article, we propose a computationally efficient approach-space (Sparse PArtial Correlation Estimation)-for selecting nonzero partial correlations under the high-dimension-low-sample-size setting. This method assumes the overall sparsity of the partial correlation matrix and employs sparse regression techniques for model fitting. We illustrate the performance of space by extensive simulation studies. It is shown that space performs well in both nonzero partial correlation selection and ...
• Hunting for Significance With the False Discovery Rate AI翻译
  作者：Posch, Martin; Zehetmayer, Sonja; Bauer, Peter
  作者单位：Medical University of Vienna
  摘要：When testing a single hypothesis. it is common knowledge that increasing the sample size after nonsignificant results and repeating the hypothesis test several times at unadjusted critical levels inflates tire overall Type I error rate severely. In contrast, if a large number of hypotheses are tested controlling the False Discovery Rate, such hunting for significance has asymptotically no impact on the error rate. More specifically. if the sample size is increased for all hypotheses simultaneo...
• Confidence Intervals for Population Ranks in the Presence of Ties and Near Ties AI翻译
  作者：Xie, Minge; Singh, Kesar; Zhang, Cun-Hui
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Frequentist confidence intervals for population ranks and their statistical justifications;ire not well established. even though here is a great need for such procedures in a practice. How do we assign confidence bounds for the ranks of health care facilities, school, and financial institution based on data that do not clearly separate the performance of different entities apart? The commonly used bootstrap-based frequentist confidence intervals and Bayesian intervals for population ranks may ...
• Intrinsically Autoregressive Spatiotemporal Models With Application to Aggregated Birth Outcomes AI翻译
  作者：Norton, Jonathan D.; Niu, Xu-Feng
  作者单位：US Food & Drug Administration (FDA); State University System of Florida; Florida State University
  摘要：A class of hierarchical Bayesian models is proposed for adverse birth outcomes such as preterm birth, which are conditional binomial distribution. The log-odds of an adverse outcome in a particular county, logit(p(i)), follow a linear model that includes observed covariates and normally-distributed random effects. Spatial dependence between neighboring regions is allowed for by including an intrinsically autoregressive (IAR) prior or air IAR convolution prior in the linear predictor. Temporal ...
• Regression Models for Identifying Noise Sources in Magnetic Resonance Images AI翻译
  作者：Zhu, Hongtu; Li, Yimei; Ibrahim, Joseph G.; Shi, Xiaoyan; An, Hongyu; Chen, Yashen; Gao, Wei; Lin, Weili; Rowe, Daniel B.; Peterson, Bradley S.
  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; New York State Psychiatry Institute; Columbia University
  摘要：Stochastic noise susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) call introduce serious bias into any Measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI)....
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions AI翻译
  作者：Johnstone, Iain M.; Lu, Arthur Yu
  作者单位：Stanford University
  摘要：Principal components analysis (PCA) is a classic method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. Contemporary datasets often have p comparable with or even much larger than n. Our main assertions, in such settings, are (a) that some initial reduction in dimensionality is desirable before applying any PCA-type search for principal modes, and (b) the initial reduction in dimensionality is best achieved by working in a basi...
• Prior Distributions From Pseudo-Likelihoods in the Presence of Nuisance Parameters AI翻译
  作者：Ventura, Laura; Cabras, Stefano; Racugno, Walter
  作者单位：University of Padua; University of Cagliari
  摘要：Consider a mode parameterized by 0 = (psi, lambda), where psi is the parameter of interest. The problern of eliminating the nuisance parameter lambda the nuis can be tackled by resorting to a pseudo-likelihood function L*(psi) for psi-namely a function of psi only and the data y with properties similar to those of a likelihood function. If one treats L*(psi) as a true likelihood. the posterior distribution pi*(psi vertical bar y) alpha pi(psi)L*(psi) for psi can be considered. where pi(psi) is...
• Bayesian Mixture Labeling by Highest Posterior Density AI翻译
  作者：Yao, Weixin; Lindsay, Bruce G.
  作者单位：Kansas State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：A fundamental problem for Bayesian mixture model analysis is label switching, which occurs as a result of the nonidentifiability of the mixture components under symmetric priors. We propose two labeling methods to solve this problem. The first method, denoted by PM(ALG), is based on the posterior modes and an ascending algorithm generically denoted ALG. We use each Markov chain Monte Carlo sample as the starting point in an ascending algorithm, and label the sample based on the mode of the pos...