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Asymptotic properties of ordinary least squares estimators in Gauss regression for random fields

Abstract

We consider ordinary least squares estimation of unknown parameters from observations of a Gaussian random field in the rectangle [O, T]×[O, S]. Strong consistency and asymptotic normality of the estimators is proved.

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Literature Cited

  1. 1.

    A. Ya. Dorogovtsev, Theory of Parameter Estimation for Stochastic Processes [in Russian], Vishcha Shkola, Kiev (1982).

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Additional information

Translated from Kibernetika, No. 5, pp. 64–68, September–October, 1989.

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Knopov, P.S., Kasitskaya, E.I. Asymptotic properties of ordinary least squares estimators in Gauss regression for random fields. Cybern Syst Anal 25, 641–648 (1989). https://doi.org/10.1007/BF01075222

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Keywords

  • Operating System
  • Artificial Intelligence
  • System Theory
  • Unknown Parameter
  • Random Field