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Abstrakty
Finite mixtures of probability distributions may be successfully used in the modeling of probability distributions of incomes. These distributions are typically heavy tailed and positively skewed. This article deals with the problem of determining the number of components in mixture modeling. This paper considers the likelihood of ratio-based testing of the null hypothesis of homogeneity in mixture models. The number of components is an important parameter in the applications of finite mixture models.(original abstract)
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autor
- University of Economics in Katowice, Poland
Bibliografia
- Benaglia T., Chauveau D., Hunter D.R., Young D.S. (2009). An R Package for Analyzing Finite Mixture Models. Journal of Statistical Software.
- Chen J., Chen H. (2001). The Likelihood ratio test for homogeneity in finite mixture models. The Canadian Journal of Statistics.
- Chen J., Li P. (2009). Hypothesis test for normal mixture models: the EM Approach. The Annals of Statistics.
- Fisz M. (1969). Rachunek prawdopodobieństwa i statystyka matematyczna. PWN. Warszawa.
- Jajuga K. (1990). Statystyczna teoria rozpoznawania obrazów. PWN. Warszawa.
- Kot S.M. (Ed.) (1999). Analiza ekonometryczna kształtowania się płac w Polsce w okresie transformacji. PWN. Warszawa.
- Li, P., Chen, J., Marriott, P. (2009) Non-finite Fisher information and homegenity: an EM approach, Biometrica 96, 411-426
- Mc Donald J.B., Jensen B. (1979). An analysis of some properties of alternative measures of income inequality based on the gamma distribution function. Journal of the American Statistical Association.
- Titterington D.M., Smith A.F., Makov U.E. (1985). Statistical analysis of finite mixture distributions. Wiley.
- Wong T.S.T., Li W.K. (2012). Test for homogeneity in gamma mixture models using likelihood ratio. Research Report. The University of Hong Kong.
Typ dokumentu
Bibliografia
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bwmeta1.element.ekon-element-000171393409