Predictions in nonlinear regression models.
Štulajter, F. (1997)
Acta Mathematica Universitatis Comenianae. New Series
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Displaying similar documents to “On estimation of a covariance function of stationary errors in a nonlinear regression model.”
Predictions in nonlinear regression models.
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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Mean square error matrix of an approximate least squares estimator in a nonlinear regression model with correlated errors.
Štulajter, F. (1992)
Acta Mathematica Universitatis Comenianae. New Series
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Adaptive trimmed likelihood estimation in regression
Tadeusz Bednarski, Brenton R. Clarke, Daniel Schubert (2010)
Discussiones Mathematicae Probability and Statistics
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In this paper we derive an asymptotic normality result for an adaptive trimmed likelihood estimator of regression starting from initial high breakdownpoint robust regression estimates. The approach leads to quickly and easily computed robust and efficient estimates for regression. A highlight of the method is that it tends automatically in one algorithm to expose the outliers and give least squares estimates with the outliers removed. The idea is to begin with a rapidly computed consistent...
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.
On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints
Sandra Donevska, Eva Fišerová, Karel Hron (2011)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Orthogonal regression, also known as the total least squares method, regression with errors-in variables or as a calibration problem, analyzes linear relationship between variables. Comparing to the standard regression, both dependent and explanatory variables account for measurement errors. Through this paper we shortly discuss the orthogonal least squares, the least squares and the maximum likelihood methods for estimation of the orthogonal regression line. We also show that all mentioned...