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| http://dx.doi.org/10.1002/jmv.26586 | DOI Listing |
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A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.
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January 2024
Purdue University, West Lafayette, IN 47907, United States of America.
Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works in graphical modeling focus on the cases that the data are Gaussian or mixed and the variables are linearly dependent. In this paper, we propose a double regression method for learning graphical models under the high-dimensional nonlinear and non-Gaussian setting, and prove that the proposed method is consistent under mild conditions.
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Maternal Fetal Medicine, Michigan State University College of Human Medicine/Corewell Health, Grand Rapids, USA.
The noninvasive prenatal test (NIPT) for genetic screening has been adopted globally as an alternative to first-trimester and quad screening due to its high sensitivity and specificity. NIPT involves detecting and processing foreign fetal DNA in maternal circulation to screen for fetal aneuploidy. An incidental consequence of this process is the detection of foreign tumor cell DNA in maternal circulation in otherwise asymptomatic patients.
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Boston University School of Public Health, Boston, Massachusetts, USA.
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College of Horticulture, Northwest Agriculture and Forest University, Yangling, Shaanxi 712100, China. Electronic address:
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