A practical method for segmentation and estimation of model parameters of the processes with frequently and instantly changing properties
Volume 3, Issue 1 (1992), pp. 80–87
Pub. online: 1 January 1992
Type: Research Article
Published
1 January 1992
1 January 1992
Abstract
A practical method for segmentation and estimation of model parameters of processes is proposed in this paper. A pseudo-stationary random process with instantly changing properties is divided into stationary segments. Every segment is described by an autoregressive model. A maximum likehood method is used for segmentation of the random process and estimation of unknown model parameters. An example with simulated data is presented.