High-dimensional ARMA model identification and its application to healthcare picture smoothing using a forgetting factor
| dc.contributor.author | Penm, Jack HW | |
| dc.date.accessioned | 2015-12-10T22:24:57Z | |
| dc.date.issued | 2010 | |
| dc.date.updated | 2016-02-24T08:26:47Z | |
| dc.description.abstract | In this paper a set of formulations of an N-dimensional (ND) autoregressive-moving average (ARMA) model identification method, and a two-dimensional (2D) forgetting factor approach in time-series modelling, is developed. An optimum estimation and prediction approach in healthcare picture smoothing based on a 2D ARMA modelling, has been implemented; and satisfactory results have been obtained. Our approach indicates the desirability of accurate statistical modelling of high-dimensional or periodic digital data. | |
| dc.identifier.issn | 1312-885X | |
| dc.identifier.uri | http://hdl.handle.net/1885/53284 | |
| dc.publisher | Hikari Ltd | |
| dc.source | Applied Mathematical Sciences | |
| dc.subject | Keywords: Forgetting factor; N-dimensional ARMA model | |
| dc.title | High-dimensional ARMA model identification and its application to healthcare picture smoothing using a forgetting factor | |
| dc.type | Journal article | |
| local.bibliographicCitation.issue | 21-24 | |
| local.bibliographicCitation.lastpage | 1139 | |
| local.bibliographicCitation.startpage | 1129 | |
| local.contributor.affiliation | Penm, Jack HW, College of Business and Economics, ANU | |
| local.contributor.authoruid | Penm, Jack HW, u7800853 | |
| local.description.embargo | 2037-12-31 | |
| local.description.notes | Imported from ARIES | |
| local.identifier.absfor | 010107 - Mathematical Logic, Set Theory, Lattices and Universal Algebra | |
| local.identifier.ariespublication | f2965xPUB270 | |
| local.identifier.citationvolume | 4 | |
| local.identifier.scopusID | 2-s2.0-77953342190 | |
| local.type.status | Published Version |