On the Shifted Hybrid Log-Normal Distribution
DOI:
https://doi.org/10.6092/issn.1973-2201/12904Keywords:
Hybrid log-normal distribution, Estimation, Lifetime data, Fracture toughness data, SimulationAbstract
The log-normal distribution is widely used to model positive valued data in many areas of applied research. However, sometimes the log-normal distribution does not completely satisfy the fitting expectations in every real life situations. In this paper, we introduce, investigate, and discuss a more flexible shifted hybrid log-normal distribution for which the log-normal distribution is a special case. Also, various properties, special cases and estimation procedure of the new distribution are discussed. Moreover, the performances of maximum likelihood estimators of the parameters are examined using a brief simulation study. The flexibility and performance of the newdistribution is also illustrated through two applications by fitting two real datasets of different situations.
References
N. BALAKRISHNAN, V. LEIVA, A. SANHUEZA, E. CABRERA (2009). Mixture inverse Gaussian distributions and its transformations, moments and applications. Statistics, 43, no. 1, pp. 91–104.
J. L. BENNING, D. L. BARNES (2009). The effects of scale and spatial heterogeneities on diffusion in volcanic breccias and basalts: Amchitka Island, Alaska. Journal of Contaminant Hydrology, 106, no. 3, pp. 150 – 165.
B. R. COBB, R. RUMÍ, A. SALMERÓN (2013). Inventory management with log-normal demand per unit time. Computers & Operations Research, 40, no. 7, pp. 1842 – 1851.
C. DOERR, N. BLENN, P. MIEGHEM (2013). Lognormal infection times of online information spread. PloS ONE, 8, no. 5, p. e64349.
C. FENG, W. HONGYUE, N. LU, X. TU (2013). Log transformation: application and interpretation in biomedical research. Statistics in Medicine, 32, pp. 230–239.
H. J. GALE (1967). Some examples of the application of the lognormal distribution in radiation protection. The Annals of Occupational Hygiene, 10, no. 1, pp. 39–45.
F. GALTON (1879). XII. The geometric mean, in vital and social statistics. Proceedings of the Royal Society of London, 29, no. 196-199, pp. 365–367.
P. GANDHI (2009). The flux-dependent rms variability of X-Ray binaries in the optical. The Astrophysical Journal Letters, 697, p. L167.
J. KAPTEYN, M. VAN UVEN (1916). Skew Frequency Curves in Biology and Statistics. v.2. Hoitsema Bros., Groningen.
S. KUMAZAWA, T. NUMAKUNAI (1981). A new theoretical analysis of occupational dose distributions indicating the effect of dose limits. Health physics, 41, pp. 465–475.
E. LIMPERT, W. A. STAHEL, M. ABBT (2001). Log-normal distributions across the sciences: Keys and clues. BioScience, 51, pp. 341–352.
D.MCALISTER (1879). The law of the geometric mean. Proceedings of the Royal Society of London, 29, pp. 367–376.
S. NADARAJAH, S. KOTZ (2006). q exponential is a Burr distribution. Physics Letters A, 359, pp. 577–579.
V. NETI, R. R. HOWELL (2008). Lognormal distribution of cellular uptake of radioactivity: statistical analysis of alpha-particle track autoradiography. Journal of Nuclear Medicine: official publication, Society of Nuclear Medicine, 49, pp. 1009–1016.
S. NYDELL (1919). The mean errors of the characteristics in logarithmic-normal distributions. Scandinavian Actuarial Journal, 1919, no. 1, pp. 134–144.
S. D. WICKSELL (1917). On logarithmic correlation with an application to the distribution of ages at first marriage. Meddelanden fran Lunds Astronomiska Observatorium Serie I, 84, pp. 1–22.
P. T. YUAN (1933). On the logarithmic frequency distribution and the semi-logarithmic correlation surface. The Annals of Mathematical Statistics, 4, no. 1, pp. 30–74.
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