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Moment estimators for the two-parameter M-Wright distribution

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  • Dexter Cahoy

Abstract

A formal parameter estimation procedure for the two-parameter M-Wright distribution is proposed. This procedure is necessary to make the model useful for real-world applications. Note that its generalization of the Gaussian density makes the M-Wright distribution appealing to practitioners. Closed-form estimators are also derived from the moments of the log-transformed M-Wright distributed random variable, and are shown to be asymptotically normal. Tests using simulated data indicated favorable results for our estimation procedure. Copyright Springer-Verlag 2012

Suggested Citation

  • Dexter Cahoy, 2012. "Moment estimators for the two-parameter M-Wright distribution," Computational Statistics, Springer, vol. 27(3), pages 487-497, September.
    • Handle: RePEc:spr:compst:v:27:y:2012:i:3:p:487-497
    DOI: 10.1007/s00180-011-0269-x
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    References listed on IDEAS

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    1. Gorenflo, Rudolf & Mainardi, Francesco & Vivoli, Alessandro, 2007. "Continuous-time random walk and parametric subordination in fractional diffusion," Chaos, Solitons & Fractals, Elsevier, vol. 34(1), pages 87-103.
    2. Guido Germano & Mauro Politi & Enrico Scalas & Ren'e L. Schilling, 2008. "Stochastic calculus for uncoupled continuous-time random walks," Papers 0802.3769, arXiv.org, revised Jan 2009.
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