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作者：Mejia, Amanda F.; Nebel, Mary Beth; Wang, Yikai; Caffo, Brian S.; Guo, Ying
作者单位：Indiana University System; Indiana University Bloomington; Kennedy Krieger Institute; Johns Hopkins University; Emory University; Rollins School Public Health; Johns Hopkins University
摘要：Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical ...
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作者：Lei, Jing
作者单位：Carnegie Mellon University
摘要：Cross-validation is one of the most popular model and tuning parameter selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, due to the ignorance of the uncertainty in the testing sample. We develop a novel statistically principled inference tool based on cross-validation that takes into account the uncertainty in the testing sample. This method outputs a set of highly competitive candidate models containing ...
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作者：Grantham, Neal S.; Guan, Yawen; Reich, Brian J.; Borer, Elizabeth T.; Gross, Kevin
作者单位：North Carolina State University; University of Minnesota System; University of Minnesota Twin Cities
摘要：Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli ...
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作者：Bacro, Jean-Noel; Gaetan, Carlo; Opitz, Thomas; Toulemonde, Gwladys
作者单位：Universite de Montpellier; Centre National de la Recherche Scientifique (CNRS); Universita Ca Foscari Venezia; INRAE; Universite de Montpellier; Centre National de la Recherche Scientifique (CNRS); Inria
摘要：The statistical modeling of space-time extremes in environmental applications is key to understanding complex dependence structures in original event data and to generating realistic scenarios for impact models. In this context of high-dimensional data, we propose a novel hierarchical model for high threshold exceedances defined over continuous space and time by embedding a space-time Gamma process convolution for the rate of an exponential variable, leading to asymptotic independence in space...
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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...
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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...
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作者：Candes, Emmanuel; Sabatti, Chiara
作者单位：Stanford University; Stanford University; Stanford University
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作者：Cox, D. R.
作者单位：University of Oxford
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作者：Ying, Zhiliang; Yu, Wen; Zhao, Ziqiang; Zheng, Ming
作者单位：Columbia University; Fudan University; Novartis
摘要：Doubly truncated data are found in astronomy, econometrics, and survival analysis literature. They arise when each observation is confined to an interval, that is, only those which fall within their respective intervals are observed along with the intervals. Unlike the one-sided truncation that can be handled by counting process-based approach, doubly truncated data are much more difficult to handle. In their analysis of an astronomical dataset, Efron and Petrosian proposed some nonparametric ...
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作者：Rabhi, Yassir; Bouezmarni, Taoufik
作者单位：State University of New York (SUNY) System; SUNY Cortland; University of Sherbrooke
摘要：Length-biased data are often encountered in cross-sectional surveys and prevalent-cohort studies on disease durations. Under length-biased sampling subjects with longer disease durations have greater chance to be observed. As a result, covariate values linked to the longer survivors are favored by the sampling mechanism. When the sampled durations are also subject to right censoring, the censoring is informative. Modeling dependence structure without adjusting for these issues leads to biased ...
