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Kriging and masurement errors

100%
Fazekas I., Kukush A.
Discussiones Mathematicae Probability and Statistics
|
2005
|
tom 25
|
nr 2
139-159
EN
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.
2
Content available remote

Sensitivity analysis in linear models

88%
Liu S., Ma T., Liu Y.
Special Matrices
|
2016
|
tom 4
|
nr 1
225-232
EN
In this work, we consider the general linear model or its variants with the ordinary least squares, generalised least squares or restricted least squares estimators of the regression coefficients and variance. We propose a newly unified set of definitions for local sensitivity for both situations, one for the estimators of the regression coefficients, and the other for the estimators of the variance. Based on these definitions, we present the estimators’ sensitivity results.We include brief remarks on possible links of these definitions and sensitivity results to local influence and other existing results.
3
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Statistical analysis of diabetes mellitus

75%
Drygas H.
Discussiones Mathematicae Probability and Statistics
|
2009
|
tom 29
|
nr 1
69-90
EN
This paper deals with an application of regression analysis to the regulation of the blood-sugar under diabetes mellitus. Section 2 gives a description of Gram-Schmidt orthogonalization, while Section 3 discusses the difference between Gauss-Markov estimation and Least Squares Estimation. Section 4 is devoted to the statistical analysis of the blood-sugar during the night. The response change of blood-sugar is explained by three variables: time, food and physical activity ("Bewegung"). At the beginning of the section it is shown that the proposed method was very successful in 2007.
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