LMOMENTS: Fortran routines for use with the method of L-moments. Hosking (J. R. Statist. Soc. B, 1990) and Hosking and Wallis (Regional frequency analysis: an approach based on L-moments, Cambridge University Press, 1997) have described L-moments and L-moment ratios, quantities useful in the summarization and estimation of probability distributions. This report contains details of Fortran routines that should facilitate the use of L-moment-based methods

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  1. Bastianin, Andrea; Manera, Matteo: A test of symmetry based on L-moments with an application to the business cycles of the G7 economies (2021)
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  3. Dey, Ashim Kumar; Das, Kumer Pial: Predicting federal funds rate using extreme value theory (2020)
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  6. Ateeq, Kahkashan; Qasim, Tahira Bano; Alvi, Ayesha Rehman: An extension of Rayleigh distribution and applications (2019)
  7. Barnard, Roger W.; Perera, Chamila; Surles, James G.; Trindade, A. Alexandre: The linearly decreasing stress Weibull (LDSWeibull): a new Weibull-like distribution (2019)
  8. Dey, Sanku; Nassar, Mazen; Kumar, Devendra: Alpha power transformed inverse Lindley distribution: a distribution with an upside-down bathtub-shaped hazard function (2019)
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  10. Hu, Guannan; Bódai, Tamás; Lucarini, Valerio: Effects of stochastic parametrization on extreme value statistics (2019)
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  15. Çetinkaya, Çağatay; Genç, Ali İ.: Moments of order statistics of the standard two-sided power distribution (2018)
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  17. Hussain, Tassaddaq; Bakouch, Hassan S.; Iqbal, Zafar: A new probability model for hydrologic events: properties and applications (2018)
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  19. Krishna, H.; Goel, N.: Classical and Bayesian inference in two parameter exponential distribution with randomly censored data (2018)
  20. Nassar, Mazen; Afify, Ahmed Z.; Dey, Sanku; Kumar, Devendra: A new extension of Weibull distribution: properties and different methods of estimation (2018)

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