-
作者：Candes, Emmanuel; Sabatti, Chiara
作者单位：Stanford University; Stanford University; Stanford University
-
作者：Cox, D. R.
作者单位：University of Oxford
-
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 ...
-
作者：Saha, Abhijoy; Bharath, Karthik; Kurtek, Sebastian
作者单位：University System of Ohio; Ohio State University; University of Nottingham
摘要：We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in , and the Fisher-Rao metric reduces to the standard metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variati...
-
作者：Overstall, Antony M.; Woods, David C.; Parker, Ben M.
作者单位：University of Southampton; University of West London
摘要：Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of nonlinear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations governing the transport of amino acids through cell membranes in human placentas. Decision-theoretic Bayesian design of experiments for such nonlinear models is conceptually very attractive, allowing the formal incorporation of prior knowledge to overcome th...
-
作者：Zhang, Xinyu; Zou, Guohua; Liang, Hua; Carroll, Raymond J.
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Qingdao University; Capital Normal University; George Washington University; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
摘要：Model averaging generally provides better predictions than model selection, but the existing model averaging methods cannot lead to parsimonious models. Parsimony is an especially important property when the number of parameters is large. To achieve a parsimonious model averaging coefficient estimator, we suggest a novel criterion for choosing weights. Asymptotic properties are derived in two practical scenarios: (i) one or more correct models exist in the candidate model set and (ii) all cand...
-
作者：Tang, Xiwei; Bi, Xuan; Qu, Annie
作者单位：University of Virginia; University of Minnesota System; University of Minnesota Twin Cities; University of Illinois System; University of Illinois Urbana-Champaign
摘要：This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction models assuming a homogeneous feature structure. We develop an innovative multilayer tensor learning method to incorporate heterogeneity to a higher-order tensor decomposition and predict d...
-
作者：Marra, Giampiero; Radice, Rosalba
作者单位：University of London; University College London; City St Georges, University of London; University of London
摘要：This article proposes an approach to estimate and make inference on the parameters of copula link-based survival models. The methodology allows for the margins to be specified using flexible parametric formulations for time-to-event data, the baseline survival functions to be modeled using monotonic splines, and each parameter of the assumed joint survival distribution to depend on an additive predictor incorporating several types of covariate effects. All the model's coefficients as well as t...
-
作者：Qi, Zhengling; Liu, Dacheng; Fu, Haoda; Liu, Yufeng
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Boehringer Ingelheim; Eli Lilly; Lilly Research Laboratories; University of North Carolina; University of North Carolina Chapel Hill
摘要：Estimating an optimal individualized treatment rule (ITR) based on patients' information is an important problem in precision medicine. An optimal ITR is a decision function that optimizes patients' expected clinical outcomes. Many existing methods in the literature are designed for binary treatment settings with the interest of a continuous outcome. Much less work has been done on estimating optimal ITRs in multiple treatment settings with good interpretations. In this article, we propose ang...
-
作者：Bebu, Ionut
作者单位：George Washington University
