Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors
Štulajter, František
Applications of Mathematics, Tome 36 (1991), p. 149-155 / Harvested from Czech Digital Mathematics Library
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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.

Publié le : 1991-01-01
Classification:  62J05,  62M10 
@article{104452,
     author = {Franti\v sek \v Stulajter},
     title = {Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors},
     journal = {Applications of Mathematics},
     volume = {36},
     year = {1991},
     pages = {149-155},
     zbl = {0727.62087},
     mrnumber = {1097699},
     language = {en},
     url = {http://dml.mathdoc.fr/item/104452}
}

Štulajter, František. Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors. Applications of Mathematics, Tome 36 (1991) pp. 149-155. http://gdmltest.u-ga.fr/item/104452/

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