کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1145485 1489665 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fast and adaptive sparse precision matrix estimation in high dimensions
ترجمه فارسی عنوان
برآورد ماتریس دقت سریع و سازگار با ابعاد بزرگ
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
چکیده انگلیسی


• We propose a new procedure for sparse precision matrix estimation.
• We are among the first to establish the theory of cross validation for this problem.
• The conditions are slightly weaker than an important penalized likelihood method.
• Improved numerical performance is observed in several examples.

This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive procedure based on cross validation, and establish its convergence rate under the Frobenius norm. The convergence rates under other matrix norms are also established. This method also enjoys the advantage of fast computation for large-scale problems, via a coordinate descent algorithm. Numerical merits are illustrated using both simulated and real datasets. In particular, it performs favorably on an HIV brain tissue dataset and an ADHD resting-state fMRI dataset.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 135, March 2015, Pages 153–162
نویسندگان
, ,