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On influence diagnostics in elliptical multivariate regression models with equicorrelated random errors
(ELSEVIER SCIENCE BV, 2014)
In this paper we discuss estimation and diagnostic procedures in elliptical multivariate regression models with equicorrelated random errors. Two procedures are proposed for the parameter estimation and the local influence ...
Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
(Elsevier Science, 2015-04)
In this work, we performed a quantitative structure activity relationship (QSAR) model for a family of 5-lipoxygenase (5-LOX) inhibitors using k-means clustering and linear discriminant analysis (LDA) for the selection of ...
Vector dot product used for reduced three independent variables of multivariate regression to a linear regression with one independent variable. Alcohols used like a model
(SOC CHILENA QUIMICA, 2007-09)
The aim of this work is based in the reduction of independent variables in multivariate regression analysis to one by means a vector dot product (E-3). By this way, it is omit the orthogonalized procedure to obtained valid ...
Influence diagnostics for elliptical multivariate linear regression models
(MARCEL DEKKER INC, 2003)
Support vector regression for functional data in multivariate calibration problems
(Elsevier Science BvAmsterdamHolanda, 2009)
Linear mixed models with skew-elliptical distributions: A Bayesian approach
(ELSEVIER SCIENCE BV, 2008)
Normality of random effects and error terms is a routine assumption for linear mixed models. However, such an assumption may be unrealistic, obscuring important features of within- and among-unit variation. A simple and ...
The optimal brain surgeon for pruning neural network architecture applied to multivariate calibration
(Elsevier Science BvAmsterdamHolanda, 1998)
Bayesian non-linear regression models with skew-elliptical errors: Applications to the classification of longitudinal profiles
(ELSEVIER, 2008)
Typically, the fundamental assumption in non-linear regression models is the normality of the errors. Even though this model offers great flexibility for modeling these effects, it suffers from the same lack of robustness ...