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作者：Jadhav, S.; Koul, H. L.; Lu, Q.
作者单位：Michigan State University; Michigan State University
摘要：This paper considers testing for no effect of functional covariates on response variables in multivariate regression. We use generalized estimating equations to determine the underlying parameters and establish their joint asymptotic normality. This is then used to test the significance of the effect of predictors on the vector of response variables. Simulations demonstrate the importance of considering existing correlation structures in the data. To explore the effect of treating genetic data...
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作者：Wheeler, M. W.; Dunson, D. B.; Herring, A. H.
作者单位：Centers for Disease Control & Prevention - USA; National Institute for Occupational Safety & Health (NIOSH); Duke University
摘要：We consider shape- restricted nonparametric regression on a closed set X. R, where it is reasonable to assume that the function has no more than H local extrema interior to X. Following a Bayesian approach we develop a nonparametric prior over a novel class of local extremum splines. This approach is shown to be consistent when modelling any continuously differentiable function within the class considered, and we use it to develop methods for testing hypotheses on the shape of the curve. Sampl...
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作者：Cuevas, F.; Porcu, E.; Bevilacqua, M.
作者单位：Aalborg University; Newcastle University - UK; Universidad de Valparaiso
摘要：We offer a dual view of the dimple problem related to space-time correlation functions in terms of their contours. We find that the dimple property (Kent et al., 2011) in the Gneiting class of correlations is in one-to-one correspondence with nonmonotonicity of the parametric curve describing the associated contour lines. Further, we show that given such a nonmonotonic parametric curve associated with a given level set, all the other parametric curves at smaller levels inherit the nonmonotonic...
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作者：Baba, Takamichi; Kanemori, Takayuki; Ninomiya, Yoshiyuki
作者单位：Shionogi & Company Limited; Kyushu University
摘要：For marginal structural models, which play an important role in causal inference, we consider a model selection problem within a semiparametric framework using inverse-probability-weighted estimation or doubly robust estimation. In this framework, the modelling target is a potential outcome that may be missing, so there is no classical information criterion. We define a mean squared error for treating the potential outcome and derive an asymptotic unbiased estimator as a criterion using an ign...
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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：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：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：Lin, Yuanyuan; Luo, Yang; Xie, Shangyu; Chen, Kani
作者单位：Chinese University of Hong Kong; Hong Kong University of Science & Technology; University of International Business & Economics
摘要：Semiparametric transformation models with random effects are useful in analysing recurrent and clustered data. With specified error and random effect distributions, Zeng & Lin (2007a) proved that nonparametric maximum likelihood estimators are semiparametric efficient. In this paper we consider a more general class of transformation models with random effects, under which an unknown monotonic transformation of the response is linearly related to the covariates and the random effects with unspe...
