Conditional independence graphs for multivariate autoregressive models by convex optimization: Efficient algorithms

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• 作者：Saï ; d Maanan^a ; ^{s.maanan@auckland.ac.nz} ; Bogdan Dumitrescu^b ; ^{bogdan.dumitrescu@acse.pub.ro} ; Ciprian Doru Giurcăneanu^a ; ^{c.giurcaneanu@auckland.ac.nz}
• 关键词：Vector autoregressive model ; Conditional independence ; Convex optimization ; Renormalized maximum likelihood ; Maximum entropy
• 刊名：Signal Processing
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：133
• 期：Complete
• 页码：122-134
• 全文大小：1486 K
• 卷排序：133

文摘

Conditional independence graph of a vector autoregressive (VAR) process. Efficient implementation of convex optimization algorithms for inferring the graph. Derivation of renormalized maximum likelihood criterion for VAR-order selection.