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作者：Choi, Byeong Yeob; Fine, Jason P.; Brookhart, M. Alan
作者单位：University of Texas System; University of Texas at San Antonio; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：Two-stage least squares estimation is popular for structural equation models with unmeasured confounders. In such models, both the outcome and the exposure are assumed to follow linear models conditional on the measured confounders and instrumental variable, which is related to the outcome only via its relation with the exposure. We consider data where both the outcome and the exposure may be incompletely observed, with particular attention to the case where both are censored event times. A ge...
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作者：Gao, Xin; Carroll, Raymond J.
作者单位：York University - Canada; Texas A&M University System; Texas A&M University College Station
摘要：We consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physical measurements across different experiments depending on how the predictor is measured. The goal is to select which predictors affect any of the responses, where the number of such informative predictors tends to infinity as the sample size increas...
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作者：Wang, X.; Jiang, B.; Liu, J. S.
作者单位：Harvard University
摘要：Detecting dependence between two random variables is a fundamental problem. Although the Pearson correlation coefficient is effective for capturing linear dependence, it can be entirely powerless for detecting nonlinear and/or heteroscedastic patterns. We introduce a new measure, G-squared, to test whether two univariate random variables are independent and to measure the strength of their relationship. The G-squared statistic is almost identical to the square of the Pearson correlation coeffi...
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作者：Kang, Jian; Hong, Hyokyoung G.; Li, Yi
作者单位：University of Michigan System; University of Michigan; Michigan State University
摘要：Traditional variable selection methods are compromised by overlooking useful information on covariates with similar functionality or spatial proximity, and by treating each covariate independently. Leveraging prior grouping information on covariates, we propose partition-based screening methods for ultrahigh-dimensional variables in the framework of generalized linear models. We show that partition-based screening exhibits the sure screening property with a vanishing false selection rate, and ...
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作者：Nye, Tom M. W.; Tang, Xiaoxian; Weyenberg, Grady; Yoshida, Ruriko
作者单位：Newcastle University - UK; Texas A&M University System; Texas A&M University College Station; University of Hawaii System; University Hawaii Hilo; United States Department of Defense; United States Navy; Naval Postgraduate School
摘要：Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi- dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high- dimensional data to a low- dimensional representation that preserves much of the sample's structure. However...
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作者：Kosmidis, I.; Guolo, A.; Varin, C.
作者单位：University of London; University College London; University of Padua; Universita Ca Foscari Venezia
摘要：Random-effects models are frequently used to synthesize information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, especially when the number of studies is small to moderate. The current paper shows how methodology that reduces the asymptotic bias of the maximum likelihood estimator of the variance component c...
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作者：Yuan, Huili; Xi, Ruibin; Chen, Chong; Deng, Minghua
作者单位：Peking University
摘要：Biological networks often change under different environmental and genetic conditions. In this paper, we model network change as the difference of two precision matrices and propose a novel loss function called the D-trace loss, which allows us to directly estimate the precision matrix difference without attempting to estimate the precision matrices themselves. Under a new irrepresentability condition, we show that the D-trace loss function with the lasso penalty can yield consistent estimator...
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作者：Li, Cheng; Srivastava, Sanvesh; Dunson, David B.
作者单位：National University of Singapore; University of Iowa; Duke University
摘要：Standard posterior sampling algorithms, such as Markov chain Monte Carlo procedures, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarante...
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作者：Zeng, Donglin; Gao, Fei; Lin, D. Y.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes. We show tha...
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作者：Dalal, Onkar; Rajaratnam, Bala
作者单位：Stanford University; University of California System; University of California Davis
摘要：Several methods have recently been proposed for estimating sparse Gaussian graphical models using l(1)-regularization on the inverse covariance or precision matrix. Despite recent advances, contemporary applications require even faster methods to handle ill-conditioned high-dimensional datasets. In this paper, we propose a new method for solving the sparse inverse covariance estimation problem using the alternating minimization algorithm, which effectively works as a proximal gradient algorith...
