SLOPE—Adaptive variable selection via convex optimization
نویسندگان
چکیده
منابع مشابه
Slope-adaptive Variable Selection via Convex Optimization.
We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ + z, where X has dimensions n × p with p possibly larger than n. SLOPE, short for Sorted L-One Penalized Estimation, is the solution to [Formula: see text]where λ1 ≥ λ2 ≥ … ≥ λ p ≥ 0 and [Formula: see text] are the decreasing absolute values of the entries of b. This is a convex program and we demonstrate a...
متن کاملOf ” Latent Variable Graphical Model Selection via Convex Optimization
Since recently, there have been an increasing interest in the problem of estimating a high-dimensional matrix K that can be decomposed in a sum of a sparse matrix S∗ (i.e., a matrix having only a small number of nonzero entries) and a low rank matrix L∗. This is motivated by applications in computer vision, video segmentation, computational biology, semantic indexing etc. The main contribution ...
متن کاملLatent Variable Graphical Model Selection via Convex Optimization
I want to start by congratulating Professors Chandrasekaran, Parrilo and Willsky for this fine piece of work. Their paper, hereafter referred to as CPW, addresses one of the biggest practical challenges of Gaussian graphical models—how to make inferences for a graphical model in the presence of missing variables. The difficulty comes from the fact that the validity of conditional independence r...
متن کاملDiscussion : Latent Variable Graphical Model Selection via Convex Optimization
1. Introduction. We would like to congratulate the authors for their refreshing contribution to this high-dimensional latent variables graphical model selection problem. The problem of covariance and concentration matrices is fundamentally important in several classical statistical methodolo-gies and many applications. Recently, sparse concentration matrices estimation had received considerable...
متن کاملDiscussion: Latent variable graphical model selection via convex optimization
I want to start by congratulating Professors Chandrasekaran, Parrilo and Willsky for this fine piece of work. Their paper, hereafter referred to as CPW, addresses one of the biggest practical challenges of Gaussian graphical models—how to make inferences for a graphical model in the presence of missing variables. The difficulty comes from the fact that the validity of conditional independence r...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2015
ISSN: 1932-6157
DOI: 10.1214/15-aoas842