High-dimensional ARMA model identification and its application to healthcare picture smoothing using a forgetting factor

Jack Penm

High-dimensional ARMA model identification and its application to healthcare picture smoothing using a forgetting factor

Jack Penm^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1129-1139
Number of pages	11
Journal	Applied Mathematical Sciences
Volume	4
Issue number	21-24
Publication status	Published - 2010

Cite this

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title = "High-dimensional ARMA model identification and its application to healthcare picture smoothing using a forgetting factor",

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.",

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AB - 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.

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JO - Applied Mathematical Sciences

JF - Applied Mathematical Sciences

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High-dimensional ARMA model identification and its application to healthcare picture smoothing using a forgetting factor

Abstract

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