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Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors
Applications of Mathematics, Tome 36 (1991) no. 2, pp. 149-155
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The least squres invariant quadratic estimator of an unknown covariance function of a stochastic process is defined and a sufficient condition for consistency of this estimator is derived. The mean value of the observed process is assumed to fulfil a linear regresion model. A sufficient condition for consistency of the least squares estimator of the regression parameters is derived, too.
The least squres invariant quadratic estimator of an unknown covariance function of a stochastic process is defined and a sufficient condition for consistency of this estimator is derived. The mean value of the observed process is assumed to fulfil a linear regresion model. A sufficient condition for consistency of the least squares estimator of the regression parameters is derived, too.
DOI : 10.21136/AM.1991.104452
Classification : 62J05, 62M10
Keywords: stochastic process; least squares estimators; quadratic invariant estimators; linear regression model; unknown covariance function; sufficient condition for consistency 
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Štulajter, František. Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors. Applications of Mathematics, Tome 36 (1991) no. 2, pp. 149-155. doi: 10.21136/AM.1991.104452

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