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.
|Title of host publication||Collaborative research in electronic healthcare|
|Subtitle of host publication||Bioinformatics, pharmacy informatics, and computing|
|Editors||T. J. O'Neill, J. Penm, R. D. Terrell|
|Place of Publication||Rivett|
|Number of pages||34|
|Publication status||Published - 2008|
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.