Un nuevo estimador muestral de regresión vía residuos ortogonales derivados del análisis de componentes principales (2009)
Regression estimators are tools that employ statistics techniques such as regression analysis in order to gain in eﬃciency by means of the available auxiliary information. This paper presents the theoretical approach that yields to the proposal of a new orthogonal regression estimator for which the ﬁt is not based in the theory of classical least squares, but instead, it is based in the theory of principal components which minimizes the orthogonal distances from each point of the scatter plot to the line that incorporates most of the inertia.