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Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R

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  • Su, Steve

Abstract

This paper describes the use of GLDEX in R to fit distributions to empirical data using the discretized and maximum likelihood methods. The GLDEX package also provides diagnostic tests to examine the quality of fit through the resample Kolmogorov-Smirnoff test, quantile plots and comparison of the mean, variance, skewness and kurtosis between the empirical data and the fitted distribution.

Suggested Citation

  • Su, Steve, 2007. "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i09).
    • Handle: RePEc:jss:jstsof:v:021:i09
    DOI: http://hdl.handle.net/10.18637/jss.v021.i09
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    References listed on IDEAS

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    1. Su, Steve, 2007. "Numerical maximum log likelihood estimation for generalized lambda distributions," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3983-3998, May.
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    Cited by:

    1. Steve Su, 2016. "Flexible modelling of survival curves for censored data," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-20, December.
    2. Luke A. Prendergast & Robert G. Staudte, 2017. "When large n is not enough – Distribution-free interval estimators for ratios of quantiles," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 277-293, September.
    3. Klein, Ingo, 2017. "(Generalized) maximum cumulative direct, paired, and residual Φ entropy principle," FAU Discussion Papers in Economics 25/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    4. Su, Steve, 2009. "Confidence intervals for quantiles using generalized lambda distributions," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3324-3333, July.
    5. Luke A. Prendergast & Robert G. Staudte, 2017. "When large n is not enough – Distribution-free interval estimators for ratios of quantiles," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 277-293, September.
    6. Steve Su, 2018. "The Danger of Doing Power Calculations Using Only Descriptive Statistics," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(4), pages 113-114, March.
    7. Yuzhi Cai, 2021. "Estimating expected shortfall using a quantile function model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4332-4360, July.

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