Smoothing sample extremes: The mixed model approach

Fabrizio Laurini and Francesco Pauli

Computational Statistics & Data Analysis, 2009, vol. 53, issue 11, 3842-3854

Abstract: Nonparametric regression for sample extremes can be performed using a variety of techniques. The penalized spline approach for the Poisson point process model is considered. The generalized linear mixed model representation for the spline model, with its Bayesian approach to inference, turns out to be a very flexible framework. Monte Carlo Markov chain algorithms are employed for exploration of the posterior distribution. The overall performance of the method is tested on simulated data. Two real data applications are also discussed for modeling trend of intensity of earthquakes in Italy and for assessing seasonality and short term trend of summer extreme temperatures in Milan, Italy.

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00141-8
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:11:p:3842-3854

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().