gldpy 0.2

Generalized Lambda Distribution for Python

gldpy

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gldpy is Python package that implements tools for using Generalized Lambda Distribution (GLD) of three different parameterization types.

GLD is flexible family of continuous probability distributions with wide variety of density shapes under one functional form. It specified by four parameters which determine location, scale and shape (skewness and kurtosis) of the distribution.

GLD is easily described in terms of quantile function which makes it easy to use in simulation studies.

It can approximate a lot of well-known distributions such as normal, uniform, exponential, Student's, gamma, some of beta distributions and many others.

GLD shapes can be symmetric or asymmetric and have different combination of finite and infinite tails, they include unimodal, U-shaped, J-shaped and monotone density functions. So it is particularly useful tool for data fitting applications.

For more information, see the documentation.

Usage examples

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https://github.com/alexsandi/using_gldpy

References

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1. Chalabi, Y., Scott, D.J., & Wuertz, D. 2012. Flexible distribution modeling with the generalized lambda distribution.

2. Cheng, R.C.H., & Amin, N.A.K. 1983. Estimating parameters in continuous univariate distributions with a shifted origin. Journal of the Royal Statistical Society: Series B (Methodological), 45(3), 394–403.

3. Freimer, M., Kollia, G., Mudholkar, G.S., & Lin, C.T. 1988. A study of the generalized Tukey lambda family. Communications in Statistics-Theory and Methods, 17, 3547–3567.

4. Karian, Z.A., Dudewicz, E.J. 2000. Fitting statistical distributions: the generalized lambda distribution and generalized bootstrap methods. Chapman and Hall/CRC.

5. Karvanen, J. and Nuutinen, A. 2008. Characterizing the generalized lambda distribution by L-moments. Computational Statistics & Data Analysis, 52(4):1971–1983.

6. King, R. A. R., and MacGillivray, H. L. 1999. "A Starship Estimation Method for the Generalized Lambda Distributions," Australian and New Zealand Journal of Statistics, 41, 353–374.

7. Ramberg, J.S., & Schmeiser, B.W. 1974. An approximate method for generating asymmetric random variables. Communications of the ACM, 17(2), 78–82

8. Ranneby, B. 1984. The maximum spacing method. an estimation method related to the maximum likelihood method. Scandinavian Journal of Statistics, 93–112.

9. Su, S. 2007. Numerical maximum log likelihood estimation for generalized lambda distributions. Computational Statistics & Data Analysis, 51(8), 3983–3998.

10. Van Staden, Paul J., & M.T. Loots. 2009. Method of L-moment estimation for generalized lambda distribution. Third Annual ASEARC Conference. Newcastle, Australia.