eprintid: 1458
rev_number: 15
eprint_status: archive
userid: 2
dir: disk0/00/00/14/58
datestamp: 2015-02-20 11:30:26
lastmod: 2017-02-08 12:21:35
status_changed: 2015-04-27 12:10:56
type: article
metadata_visibility: show
creators_name: Grillenzoni, Carlo
corp_creators: IUAV, Department of Planning, Santa Croce 1957, Venice 30135, Italy
title: Non-parametric smoothing of spatio-temporal point processes
ispublished: pub
subjects: MP2
divisions: EPOS-P
full_text_status: none
keywords: Bandwidth; California Earthquakes; Exponential Weighting; Kernel density and regression;
Recursive tests; Space–time non-stationarity
abstract: This paper develops adaptive non-parametric modelings for earthquake data. Non-parametric techniques are particularly suitable for space–time point processes, however they must be adapted to deal with the non-stationarity of seismic phenomena. By this we mean changes in the spatial and temporal pattern of seismic occurrences. A set of non-parametric tests, kernel density and regression estimators are proposed to study the space–time evolution of earthquakes. The implied solutions, by respecting the unidirectional nature of time and minimizing prediction errors, are naturally oriented to forecasting. An extensive application to the Northern California Earthquake Catalog (NCEC) data-set, starting from 1930, illustrates and checks the approach.
date: 2003-09-07
publication: Journal of Statistical Planning and Inference
volume: 128
number: 1
publisher: Elsevier
pagerange: 61-78
id_number: doi:10.1016/j.jspi.2003.09.030
refereed: TRUE
issn: 0378-3758
official_url: http://dx.doi.org/10.1016/j.jspi.2003.09.030
access_IS-EPOS: limited
owner: Publisher
citation:   Grillenzoni, Carlo  (2003) Non-parametric smoothing of spatio-temporal point processes.  Journal of Statistical Planning and Inference, 128 (1).  pp. 61-78. DOI: https://doi.org/10.1016/j.jspi.2003.09.030 <https://doi.org/10.1016/j.jspi.2003.09.030>