-
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...
-
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 ...
-
作者：Fogarty, Colin B.; Small, Dylan S.
作者单位：University of Pennsylvania; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
摘要：A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational studies have been well addressed when there is a single outcome variable, accounting for multiple comparisons through the existing methods yields overly conservative results when there are multiple outcome variables of interest. This stems from the fact that unmeasured confounding cannot affect the probability of assignm...
-
作者：Wallace, Michael P.; Moodie, Erica E. M.; Stephens, David A.
作者单位：McGill University; McGill University
-
作者：Wood, Simon N.; Pya, Natalya; Saefken, Benjamin
作者单位：University of Bristol; KIMEP University; University of Gottingen
-
作者：Zhang, Xinyu; Yu, Dalei; Zou, Guohua; Liang, Hua
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Yunnan University of Finance & Economics
摘要：Considering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback-Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation. We prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. We further extend our concern to the generalized linear mixed-effects model framework...
-
作者：Plumlee, Matthew; Joseph, V. Roshan; Yang, Hui
作者单位：University of Michigan System; University of Michigan
摘要：Computational modeling is a popular tool to understand a diverse set of complex systems. The output from a computational model depends on a set of parameters that are unknown to the designer, but a modeler can estimate them by collecting physical data. In the described study of the ion channels of ventricular myocytes, the parameter of interest is a function as opposed to a scalar or a set of scalars. This article develops a new modeling strategy to nonparametrically study the functional param...
-
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
-
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...
-
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...
