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作者：Wang, Linbo; Robins, James M.; Richardson, Thomas S.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle
摘要：Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures for testing the bin...
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作者：Benkeser, D.; Carone, M.; van der Laan, M. J.; Gilbert, P. B.
作者单位：Emory University; University of Washington; University of Washington Seattle; University of California System; University of California Berkeley; Fred Hutchinson Cancer Center
摘要：Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical study of the behaviour of doubly robust estimators of an average treatment effect when one of the...
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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...
