Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/7759
Title: Histograms and Associated Point Processes
Authors: Jacob, Pierre 
Oliveira, Paulo 
Issue Date: 1999
Citation: Statistical Inference for Stochastic Processes. 2:3 (1999) 227-251
Abstract: Abstract Nonparametric inference for point processes is discussed by way histograms, which provide a nice tool for the analysis of on-line data. The construction of histograms depends on a sequence of partitions, which we take to be nonembedded. This is quite natural in what regards applications, but presents some theoretical problems. In another direction, we drop the usual independence assumption on the sample, replacing it by an association assumption. Under this setting, we study the convergence of the histogram, in probability and almost surely which, under association, depends on conditions on the covariance structure. In the final section we prove that the finite dimensional distributions converge in distribution to a Gaussian centered vector with a specified covariance. The main tool of analysis is a decomposition of second order moment measures.
URI: http://hdl.handle.net/10316/7759
DOI: 10.1023/A:1009989902595
Rights: openAccess
Appears in Collections:FCTUC Matemática - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
obra.pdf149.68 kBAdobe PDFView/Open
Show full item record

Page view(s) 50

293
checked on Jul 10, 2019

Download(s) 50

433
checked on Jul 10, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.