کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1145557 1489672 2014 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Graphical model selection and estimation for high dimensional tensor data
ترجمه فارسی عنوان
انتخاب مدل گرافیکی و برآورد برای داده های تانسور با ابعاد بزرگ
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
چکیده انگلیسی

Multi-way tensor data are prevalent in many scientific areas such as genomics and biomedical imaging. We consider a KK-way tensor-normal distribution, where the precision matrix for each way has a graphical interpretation. We develop an l1l1 penalized maximum likelihood estimation and an efficient coordinate descent-based algorithm for model selection and estimation in such tensor normal graphical models. When the dimensions of the tensor are fixed, we drive the asymptotic distributions and oracle property for the proposed estimates of the precision matrices. When the dimensions diverge as the sample size goes to infinity, we present the rates of convergence of the estimates and sparsistency results. Simulation results demonstrate that the proposed estimation procedure can lead to better estimates of the precision matrices and better identifications of the graph structures defined by the precision matrices than the standard Gaussian graphical models. We illustrate the methods with an analysis of yeast gene expression data measured over different time points and under different experimental conditions.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 128, July 2014, Pages 165–185
نویسندگان
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