Displaying similar documents to “On the least quares fitting in a linear model.”

A note on the strong consistency of least squares estimates

Joǎo Lita da Silva (2009)

Discussiones Mathematicae Probability and Statistics

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The strong consistency of least squares estimates in multiples regression models with i.i.d. errors is obtained under assumptions on the design matrix and moment restrictions on the errors.

Kriging and masurement errors

István Fazekas, Alexander G. Kukush (2005)

Discussiones Mathematicae Probability and Statistics

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A linear geostatistical model is considered. Properties of a universal kriging are studied when the locations of observations aremeasured with errors. Alternative prediction procedures are introduced and their least squares errors are analyzed.

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...