Finite mixture of compositional regression with gaussian errors
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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.
- UNIVERSIDADE DE SAO PAULO Brazil
- National University of Colombia Colombia
- Universidade de São Paulo Brazil
Statistics and Probability, Robust Estimation, Social Sciences, finite mixture regression, Management Science and Operations Research, Applications of statistics, 31 Colecciones de estadística general / Statistics, Estimator, Quantum mechanics, Decision Sciences, FOS: Economics and business, Artificial Intelligence, Machine Learning for Mineral Prospectivity Mapping, FOS: Mathematics, mixtura finita, Robust Statistics, Econometrics, EM algorithm, Mixture model, Principal Component Analysis, Application of Grey System Theory in Forecasting, Physics, Statistics, Finite Mixture Regression, algoritmo EM, Compositional Data Analysis, Applied mathematics, EM Algorithm, Regression, HA1-4737, compositional data, 51 Matemáticas / Mathematics, Datos Composicionales, Computer Science, Physical Sciences, Gaussian, Compositional Data, Regression analysis, Mathematics, Detection and Handling of Multicollinearity in Regression Analysis, Forecasting Model Optimization, Maximum likelihood
Statistics and Probability, Robust Estimation, Social Sciences, finite mixture regression, Management Science and Operations Research, Applications of statistics, 31 Colecciones de estadística general / Statistics, Estimator, Quantum mechanics, Decision Sciences, FOS: Economics and business, Artificial Intelligence, Machine Learning for Mineral Prospectivity Mapping, FOS: Mathematics, mixtura finita, Robust Statistics, Econometrics, EM algorithm, Mixture model, Principal Component Analysis, Application of Grey System Theory in Forecasting, Physics, Statistics, Finite Mixture Regression, algoritmo EM, Compositional Data Analysis, Applied mathematics, EM Algorithm, Regression, HA1-4737, compositional data, 51 Matemáticas / Mathematics, Datos Composicionales, Computer Science, Physical Sciences, Gaussian, Compositional Data, Regression analysis, Mathematics, Detection and Handling of Multicollinearity in Regression Analysis, Forecasting Model Optimization, Maximum likelihood
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