Recursion-based multiple changepoint detection in multivariate linear regression and application to river streamflows

Seidou, Ousmane et Ouarda, Taha B. M. J. (2006). Recursion-based multiple changepoint detection in multivariate linear regression and application to river streamflows Rapport de recherche (R843). INRS-Eau, Terre et Environnement , Québec.

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Résumé

A large number of models in hydrology and climate sciences rely on multivariate linear regression to explain the link between key variables. The relationship in the physical world may experiment sudden changes due to climatic, environmental or anthropogenic perturbations. To deal with this issue, a Bayesian method of multiple changepoint detection in multivariate linear regression is proposed in this paper. It is an adaptation of the recursion-based multiple changepoint method of Fearnhead to the classical multivariate linear model. A new class of priors for the parameters of the multivariate linear model is introduced and useful formulas are derived that permit straightforward computation of the posterior distribution of the changepoints. The proposed method is numerically efficient and does not involve time consuming Monte-Carlo Markov Chain simulation as opposed to other Bayesian changepoint methods. It allows fast and straightforward simulation of the probability of each possible number of changepoints as well as the posterior probability distribution of each changepoint conditional on the number of changes. The approach is validated on simulated data sets and then compared to the methodology of Asselin and Ouarda [2005] on two practical problems: a) the changepoint detection in the multivariate linear relationship between mean basin scale precipitation at different periods of the year and the summer-autumn flood peaks of the Broadback River located in Northern Quebec, Canada; and b) the detection of trend variations in the streamflows of the Ogoki River located in the province of Ontario, Canada.

Type de document:	Rapport
Mots-clés libres:	Hydrologie; analyse bayésienne; cours eau; analyse; débit; régression linéaire multivariée; multivariable; Québec; Ontario
Centre:	Centre Eau Terre Environnement
Date de dépôt:	15 avr. 2013 17:59
Dernière modification:	11 janv. 2017 16:26
URI:	https://espace.inrs.ca/id/eprint/1268

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