-
作者：Huser, Raphael; Wadsworth, Jennifer L.
作者单位：King Abdullah University of Science & Technology; Lancaster University
摘要：Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as i...
-
作者：Liu, Yaowu; Xie, Jun
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Purdue University System; Purdue University
摘要：It is of fundamental interest in statistics to test the significance of a set of covariates. For example, in genome-wide association studies, a joint null hypothesis of no genetic effect is tested for a set of multiple genetic variants. The minimum p-value method, higher criticism, and Berk-Jones tests are particularly effective when the covariates with nonzero effects are sparse. However, the correlations among covariates and the nonGaussian distribution of the response pose a great challenge...
-
作者：Ni, Yang; Stingo, Francesco C.; Baladandayuthapani, Veerabhadran
作者单位：University of Texas System; University of Texas Austin; Rice University; University of Texas System; UTMD Anderson Cancer Center; University of Florence
摘要：We consider the problem of modeling conditional independence structures in heterogenous data in the presence of additional subject-level covariatestermed graphical regression. We propose a novel specification of a conditional (in)dependence function of covariateswhich allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We prov...
-
作者：Alsaker, Cody; Breidt, F. Jay; van der Woerd, Mark J.
作者单位：Colorado State University System; Colorado State University Fort Collins; Colorado State University System; Colorado State University Fort Collins
摘要：Small-angle X-ray scattering (SAXS) is a technique that yields low-resolution structural information of biological macromolecules by exposing a large ensemble of molecules in solution to a powerful X-ray beam. The beam interacts with the molecules and the intensity of the scattered beam is recorded on a detector plate. The radius of gyration for a molecule, which is a measure of the spread of its mass, can be estimated from the lowest scattering angles of SAXS data. This estimation method requ...
-
作者：Bellach, Anna; Kosorok, Michael R.; Rueschendorf, Ludger; Fine, Jason P.
作者单位：University of Copenhagen; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：Direct regression modeling of the subdistribution has become popular for analyzing data with multiple, competing event types. All general approaches so far are based on nonlikelihood-based procedures and target covariate effects on the subdistribution. We introduce a novel weighted likelihood function that allows for a direct extension of the Fine-Gray model to a broad class of semiparametric regression models. The model accommodates time-dependent covariate effects on the subdistribution haza...
-
作者：Mao, Xiaojun; Chen, Song Xi; Wong, Raymond K. W.
作者单位：Iowa State University; Peking University; Peking University; Texas A&M University System; Texas A&M University College Station
摘要：This article investigates the problem of matrix completion from the corrupted data, when the additional covariates are available. Despite being seldomly considered in the matrix completion literature, these covariates often provide valuable information for completing the unobserved entries of the high-dimensional target matrix A(0). Given a covariate matrix X with its rows representing the row covariates of A(0), we consider a column-space-decomposition model A(0) = X-0 + B-0, where (0) is a c...
-
作者：Ma, Li; Mao, Jialiang
作者单位：Duke University
摘要：We introduce a methodcalled Fisher exact scanning (FES)for testing and identifying variable dependency that generalizes Fisher's exact test on 2 x 2 contingency tables to R x C contingency tables and continuous sample spaces. FES proceeds through scanning over the sample space using windows in the form of 2 x 2 tables of various sizes, and on each window completing a Fisher's exact test. Based on a factorization of Fisher's multivariate hypergeometric (MHG) likelihood into the product of the u...
-
作者：Lee, Kwonsang; Small, Dylan S.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
摘要：Malaria is a major health problem in many tropical regions. Fever is a characteristic symptom of malaria. The fraction of fevers that are attributable to malaria, the malaria attributable fever fraction (MAFF), is an important public health measure in that the MAFF can be used to calculate the number of fevers that would be avoided if malaria was eliminated. Despite such causal interpretation, the MAFF has not been considered in the framework of causal inference. We define the MAFF using the p...
-
作者：Kennedy, Edward H.; Harris, Steve; Keele, Luke J.
作者单位：Carnegie Mellon University; University of London; Queen Mary University London; University College London; Georgetown University
摘要：Pretreatment selection or censoring (selection on treatment) can occur when two treatment levels are compared ignoring the third option of neither treatment, in censoring by death settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estima...
-
作者：Ni, Yang; Stingo, Francesco C.; Ha, Min Jin; Akbani, Rehan; Baladandayuthapani, Veerabhadran
作者单位：University of Texas System; University of Texas Austin; University of Florence; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
摘要：Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein-gene relationships as well ...
