519期

最新文章

• Mining Massive Amounts of Genomic Data: A Semiparametric Topic Modeling Approach AI翻译
  作者：Fang, Ethan X.; Li, Min-Dian; Jordan, Michael I.; Liu, Han
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; University of California System; University of California Berkeley; Princeton University
  摘要：Characterizing the functional relevance of transcription factors (TFs) in different biological contexts is pivotal in systems biology. Given the massive amount of genornic data, computational identification of TFs is emerging as a useful approach to bridge functional genorriics with disease risk loch In this article, we use large-scale gene expression and chromatin immunoprecipitation (ChIP) data corpuses to conduct high-throughput TF-biological context association analysis. This work makes tw...
• Statistical Significance and the Dichotomization of Evidence: The Relevance of the ASA Statement on Statistical Significance and p-Values for Statisticians AI翻译
  作者：Laber, Eric B.; Shedden, Kerby
  作者单位：North Carolina State University; University of Michigan System; University of Michigan
• A Functional Varying-Coefficient Single-Index Model for Functional Response Data AI翻译
  作者：Li, Jialiang; Huang, Chao; Zhu, Hongtu
  作者单位：National University of Singapore; University of North Carolina; University of North Carolina Chapel Hill; University of Texas System; UTMD Anderson Cancer Center
  摘要：Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single-index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of interest. FVCSIM represents a new extension of varying-coefficient single-index models for scalar responses collected from cross-sectional and longitudinal studies. An efficient estimation procedure is developed to iteratively estimate varying coefficient functions, link functions, ind...
• When, Where, and How of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling AI翻译
  作者：Tsionas, Mike G.
  作者单位：Lancaster University; Athens University of Economics & Business
  摘要：The issues of functional form, distributions of the error components, and endogeneity are for the most part still open in stochastic frontier models. The same is true when it comes to imposition of restrictions of mono tonicity and curvature, making efficiency estimation an elusive goal. In this article, we attempt to consider these problems simultaneously and offer practical solutions to the problems raised by Stone and addressed by Badunenko, Henderson and Kumbhakar. We provide major extensi...
• Constructing Predictive Microbial Signatures at Multiple Taxonomic Levels AI翻译
  作者：Wang, Tao; Zhao, Hongyu
  作者单位：Shanghai Jiao Tong University; Yale University
  摘要：Recent advances in DNA sequencing technology have enabled rapid advances in our understanding of the contribution of the human microbiome to many aspects of normal human physiology and disease. A major goal of human microbiome studies is the identification of important groups of microbes that are predictive of host phenotypes. However, the large number of bacterial taxa and the compositional nature of the data make this goal difficult to achieve using traditional approaches. Furthermore, the m...
• Joint Selection in Mixed Models using Regularized PQL AI翻译
  作者：Hui, Francis K. C.; Mueller, Samuel; Welsh, A. H.
  作者单位：Australian National University; University of Sydney
  摘要：The application of generalized linear mixed models presents some major challenges for both estimation, due to the intractable marginal likelihood, and model selection, as we usually want to jointly select over both fixed and random effects. We propose to overcome these challenges by combining penalized quasi-likelihood (PQL) estimation with sparsity inducing penalties on the fixed and random coefficients. The resulting approach, referred to as regularized PQL, is a computationally efficient me...
• The Substitute for p-Values AI翻译
  作者：Briggs, William M.
  摘要：If it was not obvious before, after reading McShane and Gal, the conclusion is that p-values should be proscribed. There are no good uses for them; indeed, every use either violates frequentist theory, is fallacious, or is based on a misunderstanding. A replacement for p-values is suggested, based on predictive models.
• Semiparametric Inference in a Genetic Mixture Model AI翻译
  作者：Li, Pengfei; Liu, Yukun; Qin, Jing
  作者单位：University of Waterloo; East China Normal University; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
  摘要：In genetic backcross studies, data are often collected from complex mixtures of distributions with known mixing proportions. Previous approaches to the inference of these genetic mixture models involve parameterizing the component distributions. However, model misspecification of any form is expected to have detrimental effects. We propose a semiparametric likelihood method for genetic mixture models: the empirical likelihood under the exponential tilting model assumption, in which the log rat...
• Extrinsic Local Regression on Manifold-Valued Data AI翻译
  作者：Lin, Lizhen; St Thomas, Brian; Zhu, Hongtu; Dunson, David B.
  作者单位：University of Texas System; University of Texas Austin; University of Notre Dame; Duke University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging, and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression settin...
• Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies AI翻译
  作者：He, Zihuai; Zhang, Min; Lee, Seunggeun; Smith, Jennifer A.; Kardia, Sharon L. R.; Roux, V. Diez; Mukherjee, Bhramar
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Drexel University
  摘要：We propose a generalized score type test for set-based inference for the gene-environment interaction with longitudinally measured quantitative traits. The test is robust to misspecification of within subject correlation structure and has enhanced power compared to existing alternatives. Unlike tests for marginal genetic association, set-based tests for the gene-environment interaction face the challenges of a potentially misspecified and high-dimensional main effect model under the null hypot...