کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6868795 1440035 2018 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Joint estimation of multiple Gaussian graphical models across unbalanced classes
ترجمه فارسی عنوان
برآورد مشترک مدلهای گاوسی چندگانه در کلاسهای نامتعادل
کلمات کلیدی
اکتشاف شبکه ژنی، لسو گرافیکی مشترک سازگار، برآورد ماتریس دقیق، عدم تعادل چند کلاس،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
The problem of jointly estimating unbalanced multi-class Gaussian graphical models is considered. Most existing methods require equal or similar sample sizes among classes. However, many real applications do not have similar sample sizes. Hence, the joint adaptive graphical lasso, a weighted l1 penalized approach is proposed for unbalanced multi-class problems. The joint adaptive graphical lasso approach combines information across classes so that their common characteristics can be shared during the estimation process. Regularization is also introduced into the adaptive term. Simulation studies show that the new approach performs better than existing methods in terms of false positive rate, accuracy, Mathews correlation coefficient, and false discovery rate. The advantages of the new approach are also demonstrated using a liver cancer data set.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 121, May 2018, Pages 89-103
نویسندگان
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