A transmuted version of the generalized half-normal distribution

  • Hugo S. Salinas Universidad de Atacama.
  • Yuri A. Iriarte Universidad de Antofagasta.
  • Juan M. Astorga Universidad de Atacama.

Resumen

An extension of the generalized half-normal distribution, given by Cooray and Ananda [5], is proposed and studied. We use the quadratic rank transmutation map to generate a transmuted version of the generalized half-normal distribution. We study some probability properties, discuss maximum likelihood estimation and present real data application indicating that the new distribution can improve the generalized half-normal distribution in fitting real data.

Biografía del autor/a

Hugo S. Salinas, Universidad de Atacama.
Departamento de Matemática, Facultad de Ingeniería.
Yuri A. Iriarte, Universidad de Antofagasta.
Departamento de Matemáticas, Facultad de Ciencias Básicas.
Juan M. Astorga, Universidad de Atacama.
Departamento de Tecnologías de la Energía, Facultad Tecnológica,

Citas

H. Akaike, “A new look at the statistical model identification”, IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716–723, Dec. 1974, doi: 10.1109/TAC.1974.1100705.

G. Aryal and C. Tsokos, “On the transmuted extreme value distribution with application”, Nonlinear Analysis: Theory, Methods & Applications, vol. 71, no. 12, Dec. 2009, doi: 10.1016/j.na.2009.01.168.

G. Aryal and C. Tsokos, “Transmuted weibull distribution: a generalization of the weibull probability distribution”, European Journal Of Pure and Applied Mathematics, vol. 4, no. 2, pp. 89—102, 2011. [On line]. Available: http://bit.ly/2OPBuUX

R. Byrd, P. Lu, J. Nocedal, and C. Zhu, “A limited memory algorithm for bound constrained optimization”, SIAM Journal on Scientific Computing, vol. 16, no. 5, pp. 1190–1208, Sep. 1995, doi: 10.1137/0916069.

K. Cooray and M. Ananda, “A generalization of the half-normal distribution with applications to lifetime data”, Communications in Statistics - Theory and Methods, vol. 37, no. 9, pp. 1323–1337, Mar. 2008, doi: 10.1080/03610920701826088.

J. Devore, Probabilidad y estadística para ingeniería y ciencias, vol. 6. México, DF: Thomson Learning, 2005.

R. Hogg and E. Tanis, Probability and statistical inference, 6th ed. New York, NY: Macmillan Publishing Company, c1993.

J. McDonald and R. Butler, “Regression models for positive random variables”, Journal of Econometrics, vol. 43, no. 1-2, pp. 227–251, Jan. 1990, doi: 10.1016/0304-4076(90)90118-D.

F. Merovci, “Transmuted Rayleigh distribution”, Austrian Journal of Statistics, vol. 42, no. 1, pp. 21–31, Feb. 2016, doi: 10.17713/ajs.v42i1.163.

F. Merovci, “Transmuted generalized Rayleigh distribution”, Journal of Statistics Applications & Probability, vol. 3, no. 1, pp. 9–20, Mar. 2014, doi: 10.18576/jsap/030102.

R. Core Team, “R: A language and environment for statistical computing. R foundation for statistical computing”, Global Biodiversity Information Facility, 10-Feb-2015. [Online]. Available: https://www.R-project.org

G. Schwarz, “Estimating the dimension of a model”, The Annals of Statistics, vol. 6, no. 2, pp. 461–464, Mar. 1978, doi: 10.1214/aos/1176344136.

W. Shaw and I. Buckley, “The alchemy of probability distributions: beyond Gram-Charlier expansions and a skew-kurtotic-normal distribution from a rank transmutation map”, 2009, arXiv:0901.0434.

Publicado
2019-08-14
Cómo citar
[1]
H. S. Salinas, Y. A. Iriarte, y J. M. Astorga, «A transmuted version of the generalized half-normal distribution», Proyecciones (Antofagasta, En línea), vol. 38, n.º 3, pp. 567-583, ago. 2019.
Sección
Artículos