[Forum SIS] Reminder: Seminario Giada Adelfio 23 Marzo (14:00) a Ca' Foscari

[Ilaria Prosdocimi]

Cari colleghi e colleghe,

Con grande piacere, vi ricordo del seminario di Martedì (il primo di una
serie di seminari, elencati alla pagina del gruppo
https://www.unive.it/pag/16818):

Data: 23 Marzo 2021 - ore 14:00
Titolo: Some properties of local weighted second-order statistics for
spatio-temporal point processes
Speaker: Giada Adelfio (Università degli Studi di Palermo)

Il seminario si potrà seguire tramite la piattaforma Zoom:
https://unive.zoom.us/j/82776377762
Meeting ID: 827 7637 7762 - Passcode: SanMarco1

Abstract
Spatial, temporal, and spatio-temporal point processes, and in particular
Poisson processes, are stochastic processes that are largely used to
describe and model the distribution of a wealth of real phenomena.
When a model is fitted to a set of random points, observed in a given
multidimensional space, diagnostic measures are necessary to assess the
goodness-of-fit and to evaluate the ability of that model to describe the
random point pattern behaviour. The main problem when dealing with residual
analysis for point processes is to find a correct definition of residuals.
Diagnostics of goodness-of-fit in the theory of point processes are often
considered through the transformation of data into residuals as a result of
a thinning or a rescaling procedure. We alternatively consider here
second-order statistics coming from weighted measures. Motivated by Adelfio
and Schoenberg (2010) for the spatial case, we consider here an extension
to the spatio-temporal context in addition to focussing on local
characteristics.
Then, rather than using global characteristics, we introduce local tools,
considering individual contributions of a global estimator as a measure of
clustering. Generally, the individual contributions to a global statistic
can be used to identify outlying components measuring the influence of each
contribution to the global statistic.
In particular, our proposed method assesses goodness-of-fit of
spatio-temporal models by using local weighted second-order statistics,
computed after weighting the contribution of each observed point by the
inverse of the conditional intensity function that identifies the process.
Weighted second-order statistics directly apply to data without assuming
homogeneity nor transforming the data into residuals, eliminating thus the
sampling variability due to the use of a transforming procedure. We provide
some characterisations and show a number of simulation studies.

Cordiali saluti

Ilaria Prosdocimi

----
Ilaria Prosdocimi
Assistant Professor in Statistics
Ca' Foscari University of Venice
Department of Environmental Sciences, Informatics and Statistic
prosdocimi.ilaria a gmail.com
ilaria.prosdocimi a unive.it
-------------- parte successiva --------------
Un allegato HTML è stato rimosso...
URL: <http://www.stat.unipg.it/pipermail/sis/attachments/20210321/93050a26/attachment.html>