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A subordinated stochastic process model

A subordinated stochastic process model

Palacios, Ana Paula, Marín, J. Miguel and Wiper, Michael P. (2015) A subordinated stochastic process model. In: Bayesian Statistics from Methods to Models and Applications. Springer Proceedings in Mathematics & Statistics, 126 . Springer, pp. 49-57. ISSN 2194-1009 (Print), 2194-1017 (Online) (doi:https://doi.org/10.1007/978-3-319-16238-6_5)

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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).

Item Type: Conference Proceedings
Title of Proceedings: Bayesian Statistics from Methods to Models and Applications
Uncontrolled Keywords: ABC, Gibbs sampling, Growth models, Stochastic processes, Subordination
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 02 Jun 2020 15:51
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT b
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 3
Selected for REF2021: REF 2
URI: http://gala.gre.ac.uk/id/eprint/16953

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