Finite mixture of compositional regression with gaussian errors

Taciana Shimizu,Francisco Louzada,Adriano Suzuki

doi:10.15446/rce.v41n1.63152

Abstract

In this paper, we consider to evaluate the efficiency of volleyball players according to the performance of attack, block and serve, but considering the compositional structure of the data related to the fundaments. The finite mixture of regression models better fitted the data in comparison with the usual regression model. The maximum likelihood estimates are obtained via an EM algorithm. A simulation study revels that the estimates are closer to the real values, the estimators are asymptotically unbiased for the parameters. A real Brazilian volleyball dataset related to the efficiency of the players is considered for the analysis.

Highlights

The performance of highlevel volleyball teams is considered fundamental for guarantee success at championships
This study provides a mixture compositional regression model to study the efciency volleyball players
Based on the preliminary results, one of the variables, namely y1, did not show good t for a regression model with normal errors according to the tests of normality and the Figure 1

Summary

Introduction

The performance of highlevel volleyball teams is considered fundamental for guarantee success at championships. Such performance may be related to eciency of the players at the game. The knowledge about the main factors (for instance, the eciency of the players) that aect the result of a game helps the decision-making of coaches, providing advantages for improving the skills of the teams. This is an important issue that must be analysed to contribute to the development of tactical and technical strategies. Bozhkova (2013) analyzed the eciency of the best volleyball players based on the scoring winning points and the assisting actions, concluding that the attack is the most points-winning skill within the best volleyball players in the world. Pena, Guerra, Busca & Serra (2013) evaluated skills and factors that better predicted the outcomes of a regular seasons volleyball matches based on the logistic regression

Objectives

Methods

Conclusion