-
作者：Qiao, Xinghao; Guo, Shaojun; James, Gareth M.
作者单位：University of London; London School Economics & Political Science; Renmin University of China; University of Southern California
摘要：Graphical models have attracted increasing attention in recent years, especially in settings involving high-dimensional data. In particular, Gaussian graphical models are used to model the conditional dependence structure among multiple Gaussian random variables. As a result of its computational efficiency, the graphical lasso (glasso) has become one of the most popular approaches for fitting high-dimensional graphical models. In this paper, we extend the graphical models concept to model the ...
-
作者：Willis, Amy
作者单位：University of Washington; University of Washington Seattle
摘要：Inferring evolutionary histories (phylogenetic trees) has important applications in biology, criminology, and public health. However, phylogenetic trees are complex mathematical objects that reside in a non-Euclidean space, which complicates their analysis. While our mathematical, algorithmic, and probabilistic understanding of phylogenies in their metric space is mature, rigorous inferential infrastructure is as yet undeveloped. In this manuscript, we unify recent computational and probabilis...
-
作者：Hui, Francis K. C.; You, C.; Shang, H. L.; Mueller, Samuel
作者单位：Australian National University; University of Nottingham Ningbo China; Australian National University; University of Sydney
摘要：Semiparametric regression offers a flexible framework for modeling nonlinear relationships between a response and covariates. A prime example are generalized additive models (GAMs) where splines (say) are used to approximate nonlinear functional components in conjunction with a quadratic penalty to control for overfitting. Estimation and inference are then generally performed based on the penalized likelihood, or under a mixed model framework. The penalized likelihood framework is fast but pot...
-
作者：Gan, Lingrui; Narisetty, Naveen N.; Liang, Feng
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：We consider a Bayesian framework for estimating a high-dimensional sparse precision matrix, in which adaptive shrinkage and sparsity are induced by a mixture of Laplace priors. Besides discussing our formulation from the Bayesian standpoint, we investigate the MAP (maximum a posteriori) estimator from a penalized likelihood perspective that gives rise to a new nonconvex penalty approximating the l(0) penalty. Optimal error rates for estimation consistency in terms of various matrix norms along...
-
作者：Ventz, Steffen; Cellamare, Matteo; Bacallado, Sergio; Trippa, Lorenzo
作者单位：Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard T.H. Chan School of Public Health; University of Cambridge
摘要：Most Bayesian response-adaptive designs unbalance randomization rates toward the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We discuss Bayesian uncertainty directed designs (BUD), a class of Bayesian designs in which the investigator specifies an information measure tailored to the experiment. All decisions during the trial are selected to optimize the available information a...
-
作者：Young, Jessica G.; Logan, Roger W.; Robins, James M.; Hernan, Miguel A.
作者单位：Harvard University; Harvard Medical School; Harvard Pilgrim Health Care; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Massachusetts Institute of Technology (MIT)
摘要：Researchers are often interested in using observational data to estimate the effect on a health outcome of maintaining a continuous treatment within a prespecified range over time, for example, always exercise at least 30 minutes per day. There may be many precise interventions that could achieve this range. In this article, we consider representative interventions. These are special cases of random dynamic interventions: interventions under which treatment at each time is assigned according t...
-
作者：Berchuck, Samuel I.; Mwanza, Jean-Claude; Warren, Joshua L.
作者单位：Duke University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Yale University
摘要：Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (VFs) acquired at regular intervals. VF data are characterized by a complex spatiotemporal structure due to the data generating process and ocular anatomy. Thus, advanced statistical methods are needed to make clinical determinations regarding progression status. We introduce a spatiotemporal boundary detection model t...
-
作者：Risk, Benjamin B.; Matteson, David S.; Ruppert, David
作者单位：Emory University; Cornell University
摘要：Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis (PCA) is used for dimension reduction prior to ICA (PCA+ICA), which could remove important information. The problem is that interesting independent components (ICs) could be mixed in several principal components that are discarded and then these ICs cannot be recovered. We formulate a linear non-Gaussian com...
-
作者：Zhou, Tingting; Elliott, Michael R.; Little, Roderick J. A.
作者单位：University of Michigan System; University of Michigan
-
作者：Petersen, Alexander; Mueller, Hans-Georg
作者单位：University of California System; University of California Santa Barbara; University of California System; University of California Davis
