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作者：Chen, Mengjie; Ren, Zhao; Zhao, Hongyu; Zhou, Harrison
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Yale University; Yale University
摘要：We propose an asymptotically normal and efficient procedure to estimate every finite subgraph for covariate-adjusted Gaussian graphical model. As a consequence, a confidence interval as well as p-value can be obtained for each edge. The procedure is tuning-free and enjoys easy implementation and efficient computation through parallel estimation on subgraphs or edges. We apply the asymptotic normality result to perform support recovery through edge-wise adaptive thresholding. This support recov...
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作者：Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin
作者单位：Princeton University; Columbia University; University of North Carolina; University of North Carolina Charlotte; University of Southern California
摘要：We propose a high-dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensio...
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作者：Du, Chao; Kao, Chu-Lan Michael; Kou, S. C.
作者单位：University of Virginia; National Central University; Harvard University
摘要：This article studies the estimation of a stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out an extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally and adopt a...
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作者：Huo, Zhiguang; Ding, Ying; Liu, Silvia; Oesterreich, Steffi; Tseng, George
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Magee-Womens Research Institute; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step toward this goal. In this article, we extend a sparse K-means method toward a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype ch...
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作者：Lu, Zeng-Hua
作者单位：University of South Australia
摘要：In many statistical applications of one-sided tests of multiple hypotheses researchers are often concerned not only with global tests of the intersection of individual hypotheses, but also with multiple tests of individual hypotheses. For example, in clinical trial studies researchers often need to find out the efficacy of a treatment, as well as the significance of each outcome measurement (endpoint) of the treatment. This article proposes MaxT type tests aiming at improving the global power ...
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作者：Mefford, Joel A.; Zaitlen, Noah A.; Witte, John S.
作者单位：University of California System; University of California San Francisco; University of California System; University of California San Francisco; University of California System; University of California San Francisco
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作者：Pan, Rui; Wang, Hansheng; Li, Runze
作者单位：Central University of Finance & Economics; Peking University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：This article is concerned with the problem of feature screening for multiclass linear discriminant analysis under ultrahigh-dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screeni...
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作者：Zhu, Yunzhang; Shen, Xiaotong; Ye, Changqing
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Personalized information filtering extracts the information specifically relevant to a user, predicting his/her preference over a large number of items, based on the opinions of users who think alike or its content. This problem is cast into the framework of regression and classification, where we integrate additional user-specific and content-specific predictors in partial latent models, for higher predictive accuracy. In particular, we factorize a user-over-item preference matrix into a prod...
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作者：Han, Peisong; Lawless, Jerald F.
作者单位：University of Waterloo
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作者：Chang, Won; Haran, Murali; Applegate, Patrick; Pollard, David
作者单位：University of Chicago; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Rapid retreat of ice in the Amundsen Sea sector of West Antarctica may cause drastic sea level rise, posing significant risks to populations in low-lying coastal regions. Calibration of computer models representing the behavior of the West Antarctic Ice Sheet is key for informative projections of future sea level rise. However, both the relevant observations and the model output are high-dimensional binary spatial data; existing computer model calibration methods are unable to handle such data...
