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作者：Su, Ryan; Lin, Xihong
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; University of Texas System; UTMD Anderson Cancer Center
摘要：Studying the effects of groups of single nucleotide polymorphisms (SNPs), as in a gene, genetic pathway, or network, can provide novel insight into complex diseases such as breast cancer, uncovering new genetic associations and augmenting the information that can be gleaned from studying SNPs individually. Common challenges in set-based genetic association testing include weak effect sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with individual SNP effects often rangin...
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作者：Dette, Holger; Goesnnann, Josua
作者单位：Ruhr University Bochum
摘要：In this article, we propose a new approach for sequential monitoring of a general class of parameters of a d-dimensional time series, which can be estimated by approximately linear functionals of the empirical distribution function. We consider a closed-end method, which is motivated by the likelihood ratio test principle and compare the new method with two alternative procedures. We also incorporate self-normalization such that estimation of the long-run variance is not necessary. We prove th...
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作者：Ma, Wei; Qin, Yichen; Li, Yang; Hu, Feifang
作者单位：Renmin University of China; University System of Ohio; University of Cincinnati; George Washington University
摘要：Covariate-adaptive randomization (CAR) procedures are frequently used in comparative studies to increase the covariate balance across treatment groups. However, because randomization inevitably uses the covariate information when forming balanced treatment groups, the validity of classical statistical methods after such randomization is often unclear. In this article, we derive the theoretical properties of statistical methods based on general CAR under the linear model framework. More importa...
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作者：Yu, Guan; Li, Quefeng; Shen, Dinggang; Liu, Yufeng
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Korea University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multimodality data could deliver better prediction performance than using single modality data. However, one special challenge for using multimodality data is related to block-missing data. In practice, due to dropouts or the high cost of measures, the observations of a certain modality can be missing complete...
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作者：Zhou, Tingyou; Zhu, Liping; Xu, Chen; Li, Runze
作者单位：Zhejiang University of Finance & Economics; Renmin University of China; University of Ottawa; 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：Feature screening plays an important role in the analysis of ultrahigh dimensional data. Due to complicated model structure and high noise level, existing screening methods often suffer from model misspecification and the presence of outliers. To address these issues, we introduce a new metric named cumulative divergence (CD), and develop a CD-based forward screening procedure. This forward screening method is model-free and resistant to the presence of outliers in the response. It also incorp...
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作者：Mejia, Amanda F.; Nebel, Mary Beth; Wang, Yikai; Caffo, Brian S.; Guo, Ying
作者单位：Indiana University System; Indiana University Bloomington; Kennedy Krieger Institute; Johns Hopkins University; Emory University; Rollins School Public Health; Johns Hopkins University
摘要：Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical ...
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作者：Rabhi, Yassir; Bouezmarni, Taoufik
作者单位：State University of New York (SUNY) System; SUNY Cortland; University of Sherbrooke
摘要：Length-biased data are often encountered in cross-sectional surveys and prevalent-cohort studies on disease durations. Under length-biased sampling subjects with longer disease durations have greater chance to be observed. As a result, covariate values linked to the longer survivors are favored by the sampling mechanism. When the sampled durations are also subject to right censoring, the censoring is informative. Modeling dependence structure without adjusting for these issues leads to biased ...
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作者：Chen, Ming
作者单位：Amazon.com
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作者：Lila, Eardi; Aston, John A. D.
作者单位：University of Cambridge; University of Cambridge
摘要：In functional data analysis, data are commonly assumed to be smooth functions on a fixed interval of the real line. In this work, we introduce a comprehensive framework for the analysis of functional data, whose domain is a two-dimensional manifold and the domain itself is subject to variability from sample to sample. We formulate a statistical model for such data, here called functions on surfaces, which enables a joint representation of the geometric and functional aspects, and propose an as...
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作者：Xia, Yin; Li, Lexin; Lockhart, Samuel N.; Jagust, William J.
作者单位：Fudan University; University of California System; University of California Berkeley; Wake Forest University; University of California System; University of California Berkeley; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of California System; University of California Berkeley
摘要：Multimodal integrative analysis fuses different types of data collected on the same set of experimental subjects. It is becoming a norm in many branches of scientific research, such as multi-omics and multimodal neuroimaging analysis. In this article, we address the problem of simultaneous covariance inference of associations between multiple modalities, which is of a vital interest in multimodal integrative analysis. Recognizing that there are few readily available solutions in the literature...
