A parsimonious dynamic mixture for heavy-tailed distributions

Marco Bee

Mathematics and Computers in Simulation (MATCOM), 2025, vol. 230, issue C, 193-206

Abstract: Dynamic mixture distributions are convenient models for highly skewed and heavy-tailed data. However, estimation has proved to be challenging and computationally expensive. To address this issue, we develop a more parsimonious model, based on a one-parameter weight function given by the exponential cumulative distribution function. Parameter estimation is carried out via maximum likelihood, approximate maximum likelihood and noisy cross-entropy. Simulation experiments and real-data analyses suggest that approximate maximum likelihood is the best method in terms of RMSE, albeit at a high computational cost. With respect to the version of the dynamic mixture with weight equal to the two-parameter Cauchy cumulative distribution function, the reduced flexibility of the present model is more than compensated by better statistical and computational properties.

Keywords: Dynamic mixtures; Exponential distribution; Noisy cross-entropy; Weight function (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:230:y:2025:i:c:p:193-206

DOI: 10.1016/j.matcom.2024.11.011

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