SciELO - Scientific Electronic Library Online

vol.11 issue21Least Change Secant Update Methods for Nonlinear Complementarity ProblemOptimization of Manipulability and Electricity Consumption Through the Heuristic Kalman Algorithm in Serial Manipulators author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand



Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google


Ingeniería y Ciencia

Print version ISSN 1794-9165


BURBANO MORENO, Álvaro Alexander  and  MELO MARTINEZ, Oscar Orlando. Linear Regression with Errors not Normal: Generalized Hyperbolic Secant. ing.cienc. [online]. 2015, vol.11, n.21, pp.37-50. ISSN 1794-9165.

This paper presents a study of the model of linear regression of the type y=Θx+e, where the error has generalized hyperbolic secant distribution (GHS). The method to estimate the parameters are obtained by setting maximum likelihood expressing the non-linear equations in linear form (modified likelihood). The resulting estimators are analytical expressions in terms of values of the sample and, therefore, are easily calculables. Through the application of various types of data, the methodology described above is shown, and plausible models against the true underlying distributions of data are.

Keywords : generalized secant hyperbolic distribution; classical linear model; modified maximum likelihood; least squares.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )