SciELO - Scientific Electronic Library Online

 
vol.41 issue1Estimating the Gumbel-Barnett Copula Parameter of DependenceA Bivariate Model based on Compound Negative Binomial Distribution author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

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

Share


Revista Colombiana de Estadística

Print version ISSN 0120-1751

Abstract

SHIMIZU, Taciana; LOUZADA, Francisco  and  SUZUKI, Adriano. Finite Mixture of Compositional Regression With Gaussian Errors. Rev.Colomb.Estad. [online]. 2018, vol.41, n.1, pp.75-86. ISSN 0120-1751.  https://doi.org/10.15446/rce.v41n1.63152.

In this paper, we consider to evaluate the efficiency of volleyball players according to your performance of attack, block and serve, considering the compositional structure of the data related to the fundaments of this sport. In this way, we consider a nite mixture of regression model to compositional data. The maximum likelihood estimation of this model was obtained via an EM algorithm. A simulation study reveals that the parameters are correctly recovery. In addition, the estimators are asymptotically unbiased. By considering real dataset of Brazilian volleyball competition, we show that the model proposed presents best fit than the usual regression model.

Keywords : Compositional Data; Finite Mixture Regression; EM Algorithm.

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