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作者：Cai, Tianxi; Cai, T. Tony; Zhang, Anru
作者单位：University of Pennsylvania
摘要：Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics, and electrical engineering. Current literature on matrix completion focuses primarily on-independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured rnissingness by design. Specifically, our proposed ...
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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：Kneib, Thomas
作者单位：University of Gottingen
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作者：Chen, Yang; Shen, Kuang; Shan, Shu-Ou; Kou, S. C.
作者单位：Harvard University; Massachusetts Institute of Technology (MIT); Whitehead Institute; California Institute of Technology
摘要：To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recording...
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作者：Feng, Long; Zou, Changliang; Wang, Zhaojun
作者单位：Nankai University; Nankai University
摘要：This article concerns tests for the two-sample location problem when data dimension is larger than the sample size. Existing multivariate-sign-based procedures are not robust against high dimensionality, producing tests with Type I error rates far away from nominal levels. This is mainly due to the biases from estimating location parameters. We propose,a novel test to overcome this issue by using the leave-one-out idea. The proposed test statistic is scalar-invariant and thus is particularly u...
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作者：Fogarty, Colin B.; Mikkelsen, Mark E.; Gaieski, David F.; Small, Dylan S.
作者单位：University of Pennsylvania
摘要：Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference to form confidence ...
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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 ...
