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A New Extended Mixture Skew Normal Distribution, With Applications
Una nueva mixtura de la distribución normal sesgada, con aplicaciones
DOI:
https://doi.org/10.15446/rce.v42n2.70087Keywords:
Mixture distributions, Kurtosis, Skewness, Skew normal\linebreak distribution (en)Distribuciones de mezclas, Kurtosis, Oblicuidad, Distribución normal sesgada (es)
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One of the most important property of the mixture normal distributions-model is its flexibility to accommodate various types of distribution functions (df's). We show that the mixture of the skew normal distribution and its reverse, after adding a location parameter to the skew normal distribution, and adding the same location parameter with different sign to its reverse is a family of df's that contains all the possible types of df's. Besides, it has a very remarkable wide range of the indices of skewness and kurtosis. Computational techniques using EM-type algorithms are employed for iteratively computing maximum likelihood estimates of the model parameters. Moreover, an application with a body mass index real data set is presented.
Una de las propiedades más importantes de la mezcla del modelo de distribuciones normales es su flexibilidad para acomodar varios tipos de funciones de distribución (fd). Mostramos que la mixtura de la distribución normal sesgada y su inversa, después de agregar un parámetro de ubicación a la distribución normal sesgada, y agregar el mismo parámetro de ubicación con un signo diferente a su inversa, es una familia de fd que contiene todos los tipos posibles de fd. Además, posee una muy amplia gama de índices de asimetría y curtosis. Las técnicas computacionales que se utilizan para calcular de forma iterativa las estimaciones de máxima verosimilitud de los parámetros del modelo es el algoritmo de esperanza-maximización (EM). Además, se presenta una aplicación con un conjunto de datos reales de índice de masa corporal.
References
Akaike, H., Petrov, B. N. & Csaki, F. (1973), Information theory and the maxi mum likelihood principle, in ‘Second International Symposium on Information Theory’, Budapest.
Al-Hussaini, E. K. & Ahsanullah, M. (2015), Exponentiated distributions, Atlantis Press, Paris, France.
Alzaatreh, A. (2011), A new method for generating families of continuous distributions, Ph.D. Thesis, Central Michigan University Mount Pleasant, Michigan.
Alzaatreh, A., Famoye, F. & Lee, C. (2013), ‘A new method for generating families of continuous distributions’, Metron 71, 63–79.
Arellano-Valle, R. B., Gomez, H. W. & Quintana, F. A. (2004), ‘A new class of skew-normal distribution’, Communications in Statistics-Theory and Methods 33(7), 1465–1480.
Azzalini, A. (1985), ‘A class of distributions which includes the normal ones’, Scandinavian Journal of Statistics 12, 171–178.
Azzalini, A. & Capitanio, A. (2014), The skew-normal and related families, Cambridge University Press, New York.
Barakat, H. M. (2015), ‘A new method for adding two parameters to a family of distributions with application to the normal and exponential families’, Statistical Methods & Applications 24(3), 359–372.
Barakat, H. M., Aboutahoun, A. W. & El-kadar, N. N. (2019), ‘On some generalized families arising from mixture normal distribution with applications’, Communication in Statistics-Simulation and Computation pp. 1–19. DOI: 10.1080/03610918.2018.1554110.
Barakat, H. M., Ghitany, M. E. & Al-Hussaini, E. K. (2009), ‘Asymptotic distributions of order statistics and record values under the Marshall-Olkin parameterization operation’, Communications in Statistics-Theory and Methods 38, 2267–2273.
Barakat, H. M. & Khaled, O. M. (2017), ‘Towards the establishment of a family of distributions that best fits any data set’, Communication in Statistics Simulation and Computation 46(8), 6129–6143.
Barakat, H. M. & Nigm, E. M. (2014), ‘Asymptotic distributions of order statistics and record values arising from the class of beta-generated distributions’, Statistics 48(5), 1005–1012.
Barakat, H. M., Nigm, E. M. & Harpy, M. H. (2017), ‘Limit theorems of order statistics and record values from the gamma and Kumaraswamy-generated-distributions’, Bulletin of the Malaysian Mathematical Sciences Society 40, 1055–1069.
Barreto-Souza, W., Lemonte, A. & Cordeiro, G. M. (2013), ‘General results for Marshall and Olkin’s family of distributions’, Anais da Academia Brasileira de Ciências 85, 3–21.
Box, G. E. P. & Tiao, G. C. (1973), Bayesian Inference in Statistical Analysis, Addision-Wesley, Reading, MA.
Cordeiro, G. M. & Castro, M. (2011), ‘A new family of generalized distributions’, Journal of Statistical Computation and Simulation 81(7), 883–898.
Cordeiro, G. M., Lemonte, A. J. & Ortiga, E. E. M. (2014a), ‘The Marshall-Olkin family of distributions: Mathematical properties and new models’, Journal of Statistical Theory and Practice 8, 343–366.
Cordeiro, G. M., Ortiga, E. E. M. & Silva, G. O. (2014), ‘The Kumaraswamy modified Weibull distribution: Theory and applications’, Journal of Statistical Computation and Simulation 84, 1387–1411.
Correa, M. A., Nogueira, D. A. & Ferreira, E. B. (2012), ‘Kumaraswamy normal and Azzalini’s skew normal modeling asymmetry’, Sigmae 1, 65–83.
Doğru, F. Z. & Arslan, O. (2017), ‘Robust mixture regression based on the skew t distribution’, Revista Colombiana de Estadística 40(1), 45–64.
Ellison, A. M. (1987), ‘Effect of seed dimorphism on the density-dependent dynamics of experimental populations of Atriplex triangularis (Chenopodiaceae)’, American Journal of Botany 74(8), 1280–1288.
Eugene, N., Lee, C. & Famoye, F. (2002), ‘Beta-normal distribution and its application’, Communications in Statistics-Theory and Methods 31, 497–512.
Ho, N. (2017), Parameter estimation and multilevel clustering with mixture and hierarchical models, Ph.D. Thesis, University of Michigan, Michigan.
Ho, N. & Nguyen, X. (2016), Singularity structures and impacts on parameter estimation in finite mixtures of distributions, Cornell University, arXiv:1609.02655 [math.ST].
Jones, M. C. (2004), ‘Families of distributions arising from distributions of order statistics’, Test 13(1), 1–43.
Jose, K. K. (2011), Marshall-olkin family of distributions and their applications in reliability theory, time series modeling and stress strength analysis, in ‘58th World Statistics Congress’, International Statistical Institute, Dublin, pp. 3918–3923.
Lemonte, A. J. (2014), ‘The beta log-logistic distribution’, Brazilian Journal of Probability and Statistics 28, 313–332.
Mameli, V. & Musio, M. (2013), ‘Generalization of the Skew-Normal Distribution: The Beta skew-normal’, Communications in Statistics-Theory and Methods 42(12), 2229–2244.
Marshall, A. W. & Olkin, I. (1997), ‘A new method for adding a prameter to a family of distributions with application to the exponential and Weibull families’, Biometrika 84, 641–652.
Martínez-Flórez, G., Vergara-Cardozo, S. & González, L. M. (2013), ‘The family of log-skew-normal alpha-power distributions using precipitation data’, Revista Colombiana de Estadística 36(1), 43–57.
McLachlan, G. & Peel, D. (2000), Finite mixture models, Wiley Series in Probability and Statististics, New York.
Nadarajah, S. (2005), ‘A generalized normal distribution’, Journal of Applied Statistics 32(7), 685–694.
Pérez-Rodríguez, P., Villaseñor, J. A., Pérezc, S. & Suárez, J. (2017), ‘Bayesian estimation for the centered parameterization of the skew-normal distribution’, Revista Colombiana de Estadística 40(1), 123–140.
Popović, B. V., Cordeiro, G. M. & Pascoa, M. A. R. (2017), ‘A new extended mixture normal distribution’, Mathematical Communications 22, 53–73.
Schwarz, G. (1978), ‘Estimating the dimension of a model’, Annals of Statistics 6(2), 461–464.
Titterington, D. M., Smith, A. F. M. & Makov, U. E. (1986), Statistical analysis of finite mixture distributions, Wiley, New York.
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1. A.I. Krasilnikov. (2023). Classification of Models of Two-component Mixtures of Symmetrical Distributions with Zero Kurtosis Coefficient. Èlektronnoe modelirovanie, 45(5), p.20. https://doi.org/10.15407/emodel.45.05.020.
2. H. M. Barakat, M. H. Dwes. (2022). Asymptotic behavior of ordered random variables in mixture of two Gaussian sequences with random index. AIMS Mathematics, 7(10), p.19306. https://doi.org/10.3934/math.20221060.
3. A.I. Krasilnikov. (2024). Modeling of Two-component Mixtures of Shifted Distributions with Zero Cumulant Coefficients. Èlektronnoe modelirovanie, 46(4), p.19. https://doi.org/10.15407/emodel.46.04.019.
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