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Profile Likelihood Estimation of the Vulnerability P (X>v) and the Mixing Proportion p Parameters in the Gumbel Mixture Model ; Estimación de verosimilitud perfil de los parámetros de vulnerabilidad P(X>v) y proporción de mezcla p en el modelo Gumbel de mezclas

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  • معلومة اضافية
    • بيانات النشر:
      Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Estadística
    • الموضوع:
      2013
    • Collection:
      Universidad Nacional de Colombia: Portal de Revistas UN
    • نبذة مختصرة :
      This paper concerns to the problem of making inferences about the vul- nerability θ = P (X>v) and the mixing proportion p parameters, when the random variable X is distributed as a mixture of two Gumbel distributions and v is a known fixed value. A profile likelihood approach is proposed for the estimation of these parameters. This approach is a powerful though sim- ple method for separately estimating a parameter of interest in the presence of unknown nuisance parameters. Inferences about θ, p or (θ, p) are given in terms of profile likelihood regions and can be easily obtained on a computer. This methodology is illustrated through a real problem where the main pur- pose is to model the size of non-metallic inclusions in steel. ; En este artículo consideramos el problema de hacer inferencias sobre el parámetro de vulnerabilidad θ= P(X>v) y la proporción de mezcla p cuando X es una variable aleatoria cuya distribución es una mezcla de dos distribuciones Gumbel y v es un valor fijo y conocido. Se propone el enfoque de verosimilitud perfil para estimar estos parámetros, el cual es un método simple, pero poderoso, para estimar por separado un parámetro de interés en presencia de parámetros de estorbo desconocidos. Las inferencias sobre θ, p o (θ; p) se presentan por medio de regiones de verosimilitud perfil y se pueden obtener fácilmente en una computadora. Esta metodología se ilustra mediante un problema real donde se modela el tamaño de inclusiones no metálicas en el acero.
    • File Description:
      application/pdf; text/html
    • Relation:
      https://revistas.unal.edu.co/index.php/estad/article/view/44343/47652; https://revistas.unal.edu.co/index.php/estad/article/view/44343/61784; Ahmad, K. E., Jaheen, Z. F. & Modhesh, A. A. (2010), ‘Estimation of a discriminant function based on small sample size from a mixture of two Gumbel distributions’, Communications in Statistics-Simulation and Computation 39, 713–725.; Barrera-Núñez, V., Meléndez-Frigola, J. & Herraiz-Jaramillo, S. (2008), A survey on voltage sag events in power systems, in ‘Transmission and Distribution Conference and Exposition: Latin America, 2008 IEEE/PES’, pp. 1–3.; Beretta, S. & Murakami, Y. (2001), ‘Largest-extreme-value distribution analysis of multiple inclusion types in determining steel cleanliness’, Metallurgical and Materials Transactions B 32, 517–523.; Cheng, R. C. H. & Iles, T. C. (1990), ‘Embedded models in three-parameter distributions and their estimation’, Journal of the Royal Statistical Society. Series B 52(1), 135–149.; Evans, M., Hastings, N. & Peacock, B. (1993), Statistical Distributions, John Wiley & Sons, New York.; Figueroa, P. G. (2012), Las funciones de verosimilitud discretizada y restringida perfil en la inferencia científica, Ph.D. Thesis, Universidad de Sonora, Hermosillo, Sonora, México.; Green, E. J., Roesch, F. A. J., Smith, A. F. M. & Strawderman, W. E. (1994), ‘Bayesian estimation for the three-parameter Weibull distribution with tree diameter data’, Biometrics 50(1), 254–269.; Johnson, N. L., Kotz, S. & Balakrishnan, N. (1994), Continuous Univariate Distributions, Vol. 2, John Wiley & Sons, New York.; Kalbfleisch, J. G. (1985), Probability and Statistical Inference, Vol. 2, Springer-Verlag, New York.; Kotz, S. & Nadarajah, S. (2000), Extreme Value Distributions. Theory and Applications, Imperial College Press, London.; Lindsay, B. (1995), Mixture Models: Theory, Geometry and Applications, Institute for Mathematical Statistics, Hayward.; Lindsey, J. K. (1999), ‘Some statistical heresies’, The Statistician 48(1), 1–40.; Lund, T., Johansson, S. & Olund, L. (1998), Nucleation of fatigue in very low oxygen bearing steels, in ‘Bearing Steels: Into the 21st Century, STP 1327’, American Society for Testing and Materials, West Conshohocken, pp. 124–130.; Montoya, J. A., Díaz-Francés, E. & Sprott, D. A. (2009), ‘On a criticism of the profile likelihood function’, Statistical Papers 50, 195–202.; Murakami, Y. (1994), ‘Inclusion rating by statistics of extreme values and its application to fatigue strength prediction and quality control of materials’, Journal of Research of the National Institute of Standards and Technology 99, 345–351.; Murray, A. T. & Grubesic, T. H. (2007), Critical Infrastructure: Reliability and Vulnerability, Springer-Verlag, Berlin.; Raynal, J. & Guevara, J. (1997), ‘Maximum likelihood estimators for the two populations Gumbel distribution’, Hydrological Science and Technology Journal 13, 47–56.; Rosas-Casals, M., Valverde, S. & Solé, R. V. (2007), ‘Topological vulnerability of the European power grid under errors and attacks’, International Journal of Bifurcation and Chaos 17(7), 2465–2475.; Serfling, R. J. (1980), Approximation Theorems of Mathematical Statistics, John Wiley & Sons, New York.; Smith, R. L. & Naylor, J. C. (1987), ‘Statistics of the three-parameter Weibull distribution’, Annals of Operations Research 9, 577–587.; Sprott, D. A. (1980), ‘Maximum likelihood and small samples: Estimation in the presence of nuisance parameters’, Biometrika 67, 515–523.; Sprott, D. A. (2000), Statistical Inference in Science, Springer-Verlag, New York.; Tartaglia, V., Caporali, E., Cavigli, E. & Moro, A. (2005), ‘L-moments based assessment of a mixture model for frequency analysis of rainfall extremes’, Advances in Geosciences 2, 331–334.; Titterington, D., Smith, A. & Makov, U. (1985), Statistical Analysis of Finite Mixture Distributions, John Wiley & Sons, New York.; Zheng, H. (2007), Investigation of power system blackouts and reliability improvement for power distribution systems, Ph.D. Thesis, The University of Texas, Arlington.; https://revistas.unal.edu.co/index.php/estad/article/view/44343
    • الدخول الالكتروني :
      https://revistas.unal.edu.co/index.php/estad/article/view/44343
    • Rights:
      Derechos de autor 2013 Revista Colombiana de Estadística ; https://creativecommons.org/licenses/by/4.0
    • الرقم المعرف:
      edsbas.C68DD1B5