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Geometric view of measurement errors
(Taylor and Francis, 2011)
The slope of the best fit line from minimizing the sum of the squared oblique errors is the root of a
polynomial of degree four. This geometric view of measurement errors is used to give insight into the
performance of ...
Mitigating collinearity in linear regression models using ridge, surrogate and raised estimators
(Cogent OA, 2016)
Collinearity in the design matrix is a frequent problem in linear regression models, for example, with economic or medical data. Previous standard procedures to mitigate the effects of collinearity included ridge regression ...
Response surface designs using the generalized variance inflation factors
(Cogent OA, 2015)
We study response surface designs using the generalized variance inflation factors for subsets as an extension of the variance inflation factors.
Minimizing oblique errors for robust estimating
(Irish Mathematical Society, 2008)
The slope of the best fit line from minimizing the sum of the squared oblique errors is shown to be the root of a polynomial of degree four. We introduce a median estimator for the slope and, using a case study, we show ...
Anomalies of the magnitude of the bias of the maximum likelihood estimator of the regression slope
(Athens Institute for Education and Research, 2015)
The slope of the best-fit line y h x x 0 1 ( ) from minimizing a function of the squared vertical and horizontal errors is the root of a polynomial of degree four which has exactly two real roots, one positive and ...
An investigation of the performance of five different estimators in the measurement error regression model
(Athens Institute for Education and Research, 2015)
In a comprehensive paper by Riggs et al.(1978) the authors analyse the performances of numerous estimators for the regression slope in the measurement error model with positive measurement error variances >0 0 for X and ...