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Perfect simulation for correlated poisson random variables conditioned to be positive

Yuzhi Cai Orcid Logo, Wilfrid Kendall

Statistics and Computing, Volume: 12, Pages: 229 - 243

Swansea University Author: Yuzhi Cai Orcid Logo

Abstract

In this paper we present a perfect simulation method for obtaining perfect samples from collections of correlated Poisson random variables conditioned to be positive. We show how to use this method to produce a perfect sample from a Boolean model conditioned to cover a set of points: in \cite{Kendal...

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Published in: Statistics and Computing
Published: 2002
URI: https://cronfa.swan.ac.uk/Record/cronfa15300
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last_indexed 2018-02-09T04:47:09Z
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spelling 2016-05-01T15:40:38.7691328 v2 15300 2013-07-30 Perfect simulation for correlated poisson random variables conditioned to be positive eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2013-07-30 BAF In this paper we present a perfect simulation method for obtaining perfect samples from collections of correlated Poisson random variables conditioned to be positive. We show how to use this method to produce a perfect sample from a Boolean model conditioned to cover a set of points: in \cite{KendallThoennes-1997} this special case was treated in a more complicated way. The method is applied to several simple examples where exact calculations can be made, so as to check correctness of the program using \(\chi ^2\)-tests, and some small-scale experiments are carried out to explore the behaviour of the conditioned Boolean model. Journal Article Statistics and Computing 12 229 243 31 7 2002 2002-07-31 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2016-05-01T15:40:38.7691328 2013-07-30T11:21:39.5151351 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 Wilfrid Kendall 2
title Perfect simulation for correlated poisson random variables conditioned to be positive
spellingShingle Perfect simulation for correlated poisson random variables conditioned to be positive
Yuzhi Cai
title_short Perfect simulation for correlated poisson random variables conditioned to be positive
title_full Perfect simulation for correlated poisson random variables conditioned to be positive
title_fullStr Perfect simulation for correlated poisson random variables conditioned to be positive
title_full_unstemmed Perfect simulation for correlated poisson random variables conditioned to be positive
title_sort Perfect simulation for correlated poisson random variables conditioned to be positive
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
Wilfrid Kendall
format Journal article
container_title Statistics and Computing
container_volume 12
container_start_page 229
publishDate 2002
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
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description In this paper we present a perfect simulation method for obtaining perfect samples from collections of correlated Poisson random variables conditioned to be positive. We show how to use this method to produce a perfect sample from a Boolean model conditioned to cover a set of points: in \cite{KendallThoennes-1997} this special case was treated in a more complicated way. The method is applied to several simple examples where exact calculations can be made, so as to check correctness of the program using \(\chi ^2\)-tests, and some small-scale experiments are carried out to explore the behaviour of the conditioned Boolean model.
published_date 2002-07-31T03:17:26Z
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score 11.013148

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