Identification, prediction and estimation of two dimensional ARMA modelling

Terence O'Neill, Jack Penm, Jonathan Penm, Robert Penm

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

This paper designs an identification, prediction and estimation algorithm of a two-dimensional autoregressive - moving average (ARMA) model using a two-dimensional innovation process from raw data. This model has been applied to a finite size of electronic healthcare image of human white blood cell chromosomes. An optimum smoothing approach based on this model has been implemented. The mean square error converges in 10 lines, and a steady state estimate of the embedded signal is easily reached. These results point out the desirability of accurate statistical modelling of two-dimensional or periodic digital data.
Original languageEnglish
Title of host publicationCollaborative research in electronic healthcare
Subtitle of host publicationBioinformatics, pharmacy informatics, and computing
EditorsT. J. O'Neill, J. Penm, R. D. Terrell
Place of PublicationRivett
PublisherEvergreen Publishing
Pages41-74
Number of pages34
ISBN (Print)9781921473982
Publication statusPublished - 2008

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O'Neill, T., Penm, J., Penm, J., & Penm, R. (2008). Identification, prediction and estimation of two dimensional ARMA modelling. In T. J. O'Neill, J. Penm, & R. D. Terrell (Eds.), Collaborative research in electronic healthcare: Bioinformatics, pharmacy informatics, and computing (pp. 41-74). Rivett : Evergreen Publishing.