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<DIV align=center><STRONG><FONT size=2>CICLO DE CHARLAS EN CIENCIAS DE LA
ATMOSFERA</FONT></STRONG></DIV>
<DIV align=center><STRONG><BR></STRONG><FONT size=2><FONT
size=3><STRONG>Seminario en Ciencias de la Atmosfera:<BR><BR>"Detección de
saltos artificiales en series climáticas: una nueva técnica<BR>estadística
bayesiana".<BR><BR><BR>Alexis Hannart, CNRS (LOCEAN-IPSL), Francia.<BR>Lugar de
trabajo acutal: Departamento de Ciencias de la Atmosfera y los<BR>Oceanos
(DCAO)<BR><BR><BR>Viernes 12 de Diciembre- 13:30 horas.<BR> Aula 8,
DCAO.<BR></STRONG></FONT><BR>**************************************************************************<BR></DIV></FONT>
<DIV align=justify><FONT size=2>Abstract<BR>In climatology, long instrumental
records are often affected by artificial<BR>shifts due to changes in the
measurement conditions. As these<BR>inhomogeneities usually have the same
magnitude as the signal studied, a<BR>direct analysis of the raw series can lead
to wrong conclusions.<BR>Statistical objective homogenization procedures, mostly
deriving from the<BR>so-called change point problem, are dealing with this
issue.<BR>We propose a new multiple change point detection technique. Our method
is<BR>based on the identification through bayesian decisioning of
subsequences<BR>with a unique change point. This approach enables to maintain a
low<BR>complexity while simultaneously leveraging advantages of the
bayesian<BR>framework. In particular, appropriate prior distributions enable
to<BR>introduce available empirical results on jump amplitude and frequency
in<BR>the decisioning.<BR>Technically, we assume jump occurrence a priori
follows a stochastic<BR>renewal process, with distribution of time between jumps
fitted on past<BR>observations. We use these assumptions in a Gaussian single
change point<BR>model and combine them with a quadratic cost function to
successively<BR>decide upon the existence of a jump, and infer its
characteristics in case<BR>it exists. Both can be done explicitly. Results on
simulated series lead<BR>to similar or improved performance level compared to
state of the art<BR>multiple change point detection
methods.<BR><BR>*************************************************************************<BR><BR>Los
esperamos a
todos!<BR><BR><BR>Gracias!<BR><BR>Natalia<BR><BR></FONT></DIV></BODY></HTML>