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作者：Reimherr, Matthew; Nicolae, Dan
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Chicago
摘要：Quantifying heritability is the first step in understanding the contribution of genetic variation to the risk architecture of complex human diseases and traits. Heritability can be estimated for univariate phenotypes from nonfamily data using linear mixed effects models. There is, however, no fully developed methodology for defining or estimating heritability from longitudinal studies. By examining longitudinal studies, researchers have the opportunity to better understand the genetic influenc...
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作者：Fan, Jianqing; Xue, Lingzhou; Zou, Hui
作者单位：Princeton University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities
摘要：We consider estimating multitask quantile regression under the transnormal model, with focus on high dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based l(1), penalization with positive-definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of the altern...
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作者：Miao, Wang; Ding, Peng; Geng, Zhi
作者单位：Peking University; University of California System; University of California Berkeley; Peking University; Peking University
摘要：Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that is, the missing mechanism depends on the missing values themselves even conditional on the observed data. With a nonignorable missing mechanism, the model of interest is often not identifiable without imposing further assumptions. We find that even if the missing mechanism has a known parametric form, the model is not i...
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作者：Satopaa, Ville A.; Pemantle, Robin; Ungar, Lyle H.
作者单位：INSEAD Business School; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
摘要：Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. However, when combining subjective probability estimates, heterogeneity stemming from people's cognitive or information diversity is often more important than measurement noise. This article presents a novel framework that uses partially overlapping information sources. A specific model is proposed within that framework and applied to the task of aggregating the probabilities given by a g...
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作者：Barut, Emre; Fan, Jianqing; Verhasselt, Anneleen
作者单位：George Washington University; Princeton University; Fudan University; Hasselt University
摘要：Independence screening is powerful for variable selection when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or its variants. When some prior knowledge on a certain important set of variables is available, a natural assessment on the relative importance of the other predictors is their conditional contributions to the response given the known set of variables. This results in conditional sure independence screening (CSIS). C...
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作者：Das, Ritabrata; Banerjee, Moulinath; Nan, Bin; Zheng, Huiyong
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Estimation of change-point locations in the broken-stick model has significant applications in modeling important biological phenomena. In this article, we present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. Our method, based on local smoothing in a shrinking neighborhood of each change-point, is shown via simulations to be computationally more viable than existing methods that rely on sear...
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作者：Han, Sung Won; Chen, Gong; Cheon, Myun-Seok; Zhong, Hua
作者单位：Korea University; University System of Georgia; Georgia Institute of Technology; New York University
摘要：Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed, graphical models, where all the edges are directed edges and contain. no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the a...
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作者：Bien, Jacob; Bunea, Florentina; Xiao, Luo
作者单位：Cornell University
摘要：We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator that tapers the sample covariance matrix by a Toeplitz, sparsely banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimator...
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作者：She, Yiyuan; Li, Shijie; Wu, Dapeng
作者单位：State University System of Florida; Florida State University
摘要：Recently, the robustification of principal component analysis (PCA) has attracted lots of attention from statisticians, engineers, and computer scientists. In this work, we study the type of outliers that are not necessarily apparent in the original observation space but can seriously affect the principal sub-space estimation. Based on a mathematical formulation of such transformed outliers, a novel robust orthogonal complement principal component analysis (ROC-PCA) is proposed. The framework ...
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作者：Tibshirani, Ryan J.; Taylor, Jonathan; Lockhart, Richard; Tibshirani, Robert
作者单位：Carnegie Mellon University; Carnegie Mellon University
