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Estimation in a linear multivariate measurement error model with a change point in the data

Estimation in a linear multivariate measurement error model with a change point in the data
Estimation in a linear multivariate measurement error model with a change point in the data
A linear multivariate measurement error model AX=B is considered. The errors in [A B] are row-wise finite dependent, and within each row, the errors may be correlated. Some of the columns may be observed without errors, and in addition the error covariance matrix may differ from row to row. The columns of the error matrix are united into two uncorrelated blocks, and in each block, the total covariance structure is supposed to be known up to a corresponding scalar factor. Moreover the row data are clustered into two groups, according to the behavior of the rows of true A matrix. The change point is unknown and estimated in the paper. After that, based on the method of corrected objective function, strongly consistent estimators of the scalar factors and X are constructed, as the numbers of rows in the clusters tend to infinity. Since Toeplitz/Hankel structure is allowed, the results are applicable to system identification, with a change point in the input data.
Linear errors-in-variables model, Corrected objective function, Clustering, Dynamic errors-in-variables model, Consistent estimator.
0167-9473
1167-1182
Kukush, Alexander
e379734b-2b24-4efd-a689-6427af2ad50b
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Van Huffel, Sabine
8814fa15-3922-4a5a-9ba5-c2ea63ceeaf7
Kukush, Alexander
e379734b-2b24-4efd-a689-6427af2ad50b
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Van Huffel, Sabine
8814fa15-3922-4a5a-9ba5-c2ea63ceeaf7

Kukush, Alexander, Markovsky, Ivan and Van Huffel, Sabine (2007) Estimation in a linear multivariate measurement error model with a change point in the data. Computational Statistics and Data Analysis, 52 (2), 1167-1182.

Record type: Article

Abstract

A linear multivariate measurement error model AX=B is considered. The errors in [A B] are row-wise finite dependent, and within each row, the errors may be correlated. Some of the columns may be observed without errors, and in addition the error covariance matrix may differ from row to row. The columns of the error matrix are united into two uncorrelated blocks, and in each block, the total covariance structure is supposed to be known up to a corresponding scalar factor. Moreover the row data are clustered into two groups, according to the behavior of the rows of true A matrix. The change point is unknown and estimated in the paper. After that, based on the method of corrected objective function, strongly consistent estimators of the scalar factors and X are constructed, as the numbers of rows in the clusters tend to infinity. Since Toeplitz/Hankel structure is allowed, the results are applicable to system identification, with a change point in the input data.

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More information

Published date: 15 October 2007
Keywords: Linear errors-in-variables model, Corrected objective function, Clustering, Dynamic errors-in-variables model, Consistent estimator.
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263885
URI: http://eprints.soton.ac.uk/id/eprint/263885
ISSN: 0167-9473
PURE UUID: 8ffd16ee-5451-4e4a-9a63-f7ddf2c70b98

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Date deposited: 13 Jun 2007
Last modified: 14 Mar 2024 07:38

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Contributors

Author: Alexander Kukush
Author: Ivan Markovsky
Author: Sabine Van Huffel

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