On the sequential space lattice fitting of two-dimensional subset autoregressions

Terence O'Neill, Jack Penm, R. D. Terrell

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The space lattice recursive fitting algorithm to select the optimum two-dimensional (2-D) full-order autoregressive models (AR) is generalised to apply to 2-D subset ARs, including full-order models. It is initiated by fitting all 'forward' and 'backward' one-lag 2-D ARs. The method thus allows us to develop successively all 2-D subset ARs of size κ (the number of lags with nonzero coefficient matrices) from 1 to K. Finally, the best subsets of each size with the minimum generalised residual power for that size are compared to a modified 2-D model selection criterion to find the optimum 2-D subset AR.

Original languageEnglish
Pages (from-to)1993-1998
Number of pages6
JournalApplied Mathematical Sciences
Volume5
Issue number37-40
Publication statusPublished - 2011

Fingerprint

Sequential Space
Autoregression
Set theory
Subset
Autoregressive Model
Model Selection Criteria
Coefficient

Cite this

@article{00874d5dd7144ee6abe894a577b6dfd9,
title = "On the sequential space lattice fitting of two-dimensional subset autoregressions",
abstract = "The space lattice recursive fitting algorithm to select the optimum two-dimensional (2-D) full-order autoregressive models (AR) is generalised to apply to 2-D subset ARs, including full-order models. It is initiated by fitting all 'forward' and 'backward' one-lag 2-D ARs. The method thus allows us to develop successively all 2-D subset ARs of size κ (the number of lags with nonzero coefficient matrices) from 1 to K. Finally, the best subsets of each size with the minimum generalised residual power for that size are compared to a modified 2-D model selection criterion to find the optimum 2-D subset AR.",
author = "Terence O'Neill and Jack Penm and Terrell, {R. D.}",
year = "2011",
language = "English",
volume = "5",
pages = "1993--1998",
journal = "Applied Mathematical Sciences",
issn = "1312-885X",
publisher = "Hikari Ltd.",
number = "37-40",

}

On the sequential space lattice fitting of two-dimensional subset autoregressions. / O'Neill, Terence; Penm, Jack; Terrell, R. D.

In: Applied Mathematical Sciences, Vol. 5, No. 37-40, 2011, p. 1993-1998.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - On the sequential space lattice fitting of two-dimensional subset autoregressions

AU - O'Neill, Terence

AU - Penm, Jack

AU - Terrell, R. D.

PY - 2011

Y1 - 2011

N2 - The space lattice recursive fitting algorithm to select the optimum two-dimensional (2-D) full-order autoregressive models (AR) is generalised to apply to 2-D subset ARs, including full-order models. It is initiated by fitting all 'forward' and 'backward' one-lag 2-D ARs. The method thus allows us to develop successively all 2-D subset ARs of size κ (the number of lags with nonzero coefficient matrices) from 1 to K. Finally, the best subsets of each size with the minimum generalised residual power for that size are compared to a modified 2-D model selection criterion to find the optimum 2-D subset AR.

AB - The space lattice recursive fitting algorithm to select the optimum two-dimensional (2-D) full-order autoregressive models (AR) is generalised to apply to 2-D subset ARs, including full-order models. It is initiated by fitting all 'forward' and 'backward' one-lag 2-D ARs. The method thus allows us to develop successively all 2-D subset ARs of size κ (the number of lags with nonzero coefficient matrices) from 1 to K. Finally, the best subsets of each size with the minimum generalised residual power for that size are compared to a modified 2-D model selection criterion to find the optimum 2-D subset AR.

UR - http://www.scopus.com/inward/record.url?scp=80051581894&partnerID=8YFLogxK

M3 - Article

VL - 5

SP - 1993

EP - 1998

JO - Applied Mathematical Sciences

JF - Applied Mathematical Sciences

SN - 1312-885X

IS - 37-40

ER -