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A Subordinated Stochastic Process Model

  • Ana Paula PalaciosEmail author
  • J. Miguel Marín
  • Michael P. Wiper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 126)

Abstract

We introduce a new stochastic model for non-decreasing processes which can be used to include stochastic variability into any deterministic growth function via subordination. This model is useful in many applications such as growth curves (children’s height, fish length, diameter of trees, etc.) and degradation processes (crack size, wheel degradation, laser light, etc.). One advantage of our approach is the ability to easily deal with data that are irregularly spaced in time or different curves that are observed at different moments of time. With the use of simulations and applications, we examine two approaches to Bayesian inference for our model: the first based on a Gibbs sampler and the second based on approximate Bayesian computation (ABC).

Key words

ABC Gibbs sampling Growth models Stochastic processes Subordination 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ana Paula Palacios
    • 1
    Email author
  • J. Miguel Marín
    • 2
  • Michael P. Wiper
    • 2
  1. 1.School of Computing and MathematicsPlymouth UniversityPlymouthUK
  2. 2.Department of StatisticsUniversidad Carlos III de MadridMadridSpain

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