The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Perspectives on errors-in-variables estimation for dynamic systems
Signal Processing
Paper: Identification of scalar errors-in-variables models with dynamics
Automatica (Journal of IFAC)
Editorial: Total Least Squares and Errors-in-variables Modeling
Computational Statistics & Data Analysis
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A linear multivariate measurement error model AX=B is considered. The errors in [AB] 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.