Geodesic
On the asymptotic optimality of orthoregressional estimates
Sibirskij žurnal industrialʹnoj matematiki, Tome 19 (2016) no. 4, pp. 51-60

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It is shown that the orthoregressive (STLS) parameter estimates in simultaneous linear systems (including autonomous difference equations with matrix coefficients) converge to the maximum likelihood estimates and thus become asymptotically best in the limit case of large variances of random coordinates on the manifold of solutions to the system observed with additive random perturbations.
Keywords: linear autonomous difference equation, parameter identification, orthoregressive estimate, STLS estimate, asymptotic efficiency. 
@article{SJIM_2016_19_4_a5,
     author = {A. A. Lomov},
     title = {On the asymptotic optimality of  orthoregressional estimates},
     journal = {Sibirskij \v{z}urnal industrialʹnoj matematiki},
     pages = {51--60},
     publisher = {mathdoc},
     volume = {19},
     number = {4},
     year = {2016},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/SJIM_2016_19_4_a5/}
}
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A. A. Lomov. On the asymptotic optimality of  orthoregressional estimates. Sibirskij žurnal industrialʹnoj matematiki, Tome 19 (2016) no. 4, pp. 51-60. http://geodesic.mathdoc.fr/item/SJIM_2016_19_4_a5/