529期

最新文章

• Demand Models With Random Partitions AI翻译
  作者：Smith, Adam N.; Allenby, Greg M.
  作者单位：University of London; University College London; University System of Ohio; Ohio State University
  摘要：Many economic models of consumer demand require researchers to partition sets of products or attributes prior to the analysis. These models are common in applied problems when the product space is large or spans multiple categories. While the partition is traditionally fixed a priori, we let the partition be a model parameter and propose a Bayesian method for inference. The challenge is that demand systems are commonly multivariate models that are not conditionally conjugate with respect to pa...
• A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets AI翻译
  作者：Lee, Clement; Wilkinson, Darren J.
  作者单位：Newcastle University - UK; Newcastle University - UK; Alan Turing Institute
  摘要：We present a hierarchical model of nonhomogeneous Poisson processes (NHPP) for information diffusion on online social media, in particular Twitter retweets. The retweets of each original tweet are modelled by a NHPP, for which the intensity function is a product of time-decaying components and another component that depends on the follower count of the original tweet author. The latter allows us to explain or predict the ultimate retweet count by a network centrality-related covariate. The inf...
• Big Data in Omics and Imaging: Integrated Analysis and Causal Inference. AI翻译
  作者：Chen, Oliver Y.
  作者单位：University of Oxford
• Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty AI翻译
  作者：Heaton, Matthew J.; Berrett, Candace; Pugh, Sierra; Evans, Amber; Sloan, Chantel
  作者单位：Brigham Young University; Brigham Young University
  摘要：Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003-2013 in Norfolk, Virginia, USA. For privacy reasons, child home address...
• On High-Dimensional Constrained Maximum Likelihood Inference AI翻译
  作者：Zhu, Yunzhang; Shen, Xiaotong; Pan, Wei
  作者单位：University System of Ohio; Ohio State University; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Inference in a high-dimensional situation may involve regularization of a certain form to treat overparameterization, imposing challenges to inference. The common practice of inference uses either a regularized model, as in inference after model selection, or bias-reduction known as debias. While the first ignores statistical uncertainty inherent in regularization, the second reduces the bias inbred in regularization at the expense of increased variance. In this article, we propose a constrain...
• Quantile Function on Scalar Regression Analysis for Distributional Data AI翻译
  作者：Yang, Hojin; Baladandayuthapani, Veerabhadran; Rao, Arvind U. K.; Morris, Jeffrey S.
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
  摘要：Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution of pixel intensiti...
• Cross-Validation, Risk Estimation, and Model Selection: Comment on a Paper by Rosset and Tibshirani AI翻译
  作者：Wager, Stefan
  作者单位：Stanford University; Stanford University
• Multivariate Kernel Smoothing and Its Applications. AI翻译
  作者：Wang, Qing
  作者单位：Wellesley College
• Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses AI翻译
  作者：Mathur, Maya B.; VanderWeele, Tyler J.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Stanford University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Random-effects meta-analyses of observational studies can produce biased estimates if the synthesized studies are subject to unmeasured confounding. We propose sensitivity analyses quantifying the extent to which unmeasured confounding of specified magnitude could reduce to below a certain threshold the proportion of true effect sizes that are scientifically meaningful. We also develop converse methods to estimate the strength of confounding capable of reducing the proportion of scientifically...
• Nonparametric Imputation by Data Depth AI翻译
  作者：Mozharovskyi, Pavlo; Josse, Julie; Husson, Francois
  作者单位：Universite Paris Saclay; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris; Institut Polytechnique de Paris; Ecole Polytechnique; Centre National de la Recherche Scientifique (CNRS); Universite de Rennes; Institut Agro; Institut Agro Rennes-Angers
  摘要：We present single imputation method for missing values which borrows the idea of data depth-a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly defined statistical depth function. For each single iteration, imputation reverts to optimization of quadratic, linear, or quasiconcave functions that...