Local inference by penalization method for biclustering model
type: Item
instance of: scholarly article
title: Local inference by penalization method for biclustering model
zbMATH Open document ID: 1414.62227
DOI: 10.3103/S1066530718030018
published in: Mathematical Methods of Statistics
publication date: 2018-12-05T00:00:00Z
review text: Suppose we observe a matrix \(X=(X_{ij})\in \mathbb{R}^{\mathbf n}\), \(\mathbf n= (n_1,n_2)\in\mathbb{N}^2\): \(X_{ij}=\theta_{ij}=\sigma \xi_{ij}\), \(i=1,\dots,n_1\), \(j=1,\dots, n_2\), where \(\theta= (\theta_{ij})\in \mathbb{R}^{\mathbf n}\) is an unknown high-dimensional parameter with biclustering structure, \(\xi=(\xi_{ij})\in \mathbb{R}^{\mathbf n}\) is a random matrix with \(E_\theta(\xi_{ij})=0\). The authors study the problem of estimation and uncertainty quantification for the unknown parameter in the biclustering model by using the penalization method. A particular case of the biclustering structure is the stochastic block model used in the network analysis.
reviewed by: B. L. S. Prakasa Rao
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62G15
zbMATH DE Number: 6989592
zbMATH Keywords: biclustering
zbMATH Keywords: uncertainty quantification
zbMATH Keywords: oracle rate
author: E. Belitser
author: Nurzhan Nurushev
MaRDI profile type: Publication
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full work available at URL: s1066530718030018
OpenAlex ID: W2896674357
Wikidata QID: Q129085372
description: scientific article; zbMATH DE number 6989592