Geodesic
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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MR Zbl
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 
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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