Change-point analysis aims at both detecting whether or not a sharp change has occurred, or whether several changes might have occurred, and identifying the times of any such changes. Numerous approaches to conduct a change-point analysis are available in the literature. In this paper we propose the use of Genetic Algorithms (GAs) for estimating Poisson change-point models. GAs are stochastic search and optimisation technique inspired by natural evolution. They provide a robust and flexible framework that can be applied to a wide range of learning and optimisation problems, in particular when traditional optimisation techniques break down. A data analysis on the annual number of patients with haemolytic uremic syndrome is presented, with change-point models estimated using the GA R package.
Poisson change-point models estimated by genetic algorithms
SCRUCCA, Luca
2016
Abstract
Change-point analysis aims at both detecting whether or not a sharp change has occurred, or whether several changes might have occurred, and identifying the times of any such changes. Numerous approaches to conduct a change-point analysis are available in the literature. In this paper we propose the use of Genetic Algorithms (GAs) for estimating Poisson change-point models. GAs are stochastic search and optimisation technique inspired by natural evolution. They provide a robust and flexible framework that can be applied to a wide range of learning and optimisation problems, in particular when traditional optimisation techniques break down. A data analysis on the annual number of patients with haemolytic uremic syndrome is presented, with change-point models estimated using the GA R package.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
