Abstract:
The problem of estimating the parameters of linear difference systems of equations from the observations of the segments of solutions with additive stochastic perturbations is considered. The methods of multivariate orthogonal regression are applied to obtain the estimators. The results of comparative study of the methods are exposed from the standpoint of information on linear constraints in observations. The properties of the estimators in the limit cases of a gross sample are studied. For small perturbations, a scheme for comparison of estimators by linear approximations is proposed.
Citation:
A. A. Lomov, “Orthoregressive estimates for the parameters of systems of linear difference equations”, Sib. Zh. Ind. Mat., 8:3 (2005), 102–119; J. Appl. Industr. Math., 1:1 (2007), 59–76
\Bibitem{Lom05}
\by A.~A.~Lomov
\paper Orthoregressive estimates for the parameters of systems of linear difference equations
\jour Sib. Zh. Ind. Mat.
\yr 2005
\vol 8
\issue 3
\pages 102--119
\mathnet{http://mi.mathnet.ru/sjim294}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=2221646}
\zmath{https://zbmath.org/?q=an:1106.93021}
\transl
\jour J. Appl. Industr. Math.
\yr 2007
\vol 1
\issue 1
\pages 59--76
\crossref{https://doi.org/10.1134/S1990478907010073}
Linking options:
https://www.mathnet.ru/eng/sjim294
https://www.mathnet.ru/eng/sjim/v8/i3/p102
This publication is cited in the following 5 articles:
A. A. Lomov, “On the asymptotic optimality of orthoregressional estimates”, J. Appl. Industr. Math., 10:4 (2016), 511–519
A. A. Lomov, “Joint identifiability of parameters of linear dynamic equations of a plant and disturbances”, J. Math. Sci., 221:6 (2017), 857–871
Lomov A.A., “On quantitative a priori measures of identifiability of coefficients of linear dynamic systems”, Journal of Computer and Systems Sciences International, 50:1 (2011), 1–13
A. A. Lomov, “On Local Stability in the Identification Problem for Coefficients of Linear Difference Equation”, J. Math. Sci., 188:4 (2013), 410–434
Lomov A.A., “Estimation of trends and identification of time series dynamics in short observation sections”, J. Comput. Syst. Sci. Int., 48:1 (2009), 1–13