Some limit properties of an approximate least squares estimator in a nonlinear regression model with correlated nonzero mean errors.
Kalická, J. (1996)
Acta Mathematica Universitatis Comenianae. New Series
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Displaying similar documents to “Mean square error matrix of an approximate least squares estimator in a nonlinear regression model with correlated errors.”
Some limit properties of an approximate least squares estimator in a nonlinear regression model with correlated nonzero mean errors.
On estimation of a covariance function of stationary errors in a nonlinear regression model.
Štulajter, F. (1994)
Acta Mathematica Universitatis Comenianae. New Series
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Mean squared errors of prediction by kriging in linear models with errors.
Štulajter, F. (1994)
Acta Mathematica Universitatis Comenianae. New Series
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Predictions in nonlinear regression models.
Štulajter, F. (1997)
Acta Mathematica Universitatis Comenianae. New Series
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Robustness of the best linear unbiased estimator and predictor in linear regression models
František Štulajter (1990)
Aplikace matematiky
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If is shown that in linear regression models we do not make a great mistake if we substitute some sufficiently precise approximations for the unknown covariance matrix and covariance vector in the expressions for computation of the best linear unbiased estimator and predictor.
Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors
František Štulajter (1991)
Applications of Mathematics
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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.
Least squares estimator consistency: a geometric approach
João Tiago Mexia, João Lita da Silva (2006)
Discussiones Mathematicae Probability and Statistics
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Consistency of LSE estimator in linear models is studied assuming that the error vector has radial symmetry. Generalized polar coordinates and algebraic assumptions on the design matrix are considered in the results that are established.
Ridge Estimator Revisited
Lubomír Kubáček (2012)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Bad conditioned matrix of normal equations in connection with small values of model parameters is a source of problems in parameter estimation. One solution gives the ridge estimator. Some modification of it is the aim of the paper. The behaviour of it in models with constraints is investigated as well.