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Polynomial power-Pareto quantile function models

Yuzhi Cai Orcid Logo

Extremes, Volume: 13, Issue: 3, Pages: 291 - 314

Swansea University Author: Yuzhi Cai Orcid Logo

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DOI (Published version): 10.1007/s10687-009-0089-3

Abstract

In this paper we propose a polynomial power-Pareto quantile function model and a Bayesian method for parameters estimation. We also carried out simulation studies and applied our methodology to real data sets empirically. The results show that a quantile function approach to statistical modelling is...

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Published in: Extremes
ISSN: 1386-1999
Published: Springer 2010
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URI: https://cronfa.swan.ac.uk/Record/cronfa6998
first_indexed 2013-07-23T11:57:00Z
last_indexed 2018-02-09T04:34:42Z
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spelling 2016-05-01T15:31:02.4591560 v2 6998 2012-01-31 Polynomial power-Pareto quantile function models eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-01-31 CBAE In this paper we propose a polynomial power-Pareto quantile function model and a Bayesian method for parameters estimation. We also carried out simulation studies and applied our methodology to real data sets empirically. The results show that a quantile function approach to statistical modelling is very flexible due to the properties of quantile functions, and that the combination of a power and a Pareto distribution enables us to model both the main body and the tails of a distribution, even though the mathematical form of the distribution does not exist. Our research also suggests a new approach to studying extreme values based on a whole data set rather than group maximum/minimum or exceedances above/below a proper threshold value. Journal Article Extremes 13 3 291 314 Springer 1386-1999 Bayesian methods · Quantile functions ·, Power-Pareto distribution · Simulation · Speed and stopping distance data ·,Wave and surge data 31 12 2010 2010-12-31 10.1007/s10687-009-0089-3 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2016-05-01T15:31:02.4591560 2012-01-31T11:15:35.8230000 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1
title Polynomial power-Pareto quantile function models
spellingShingle Polynomial power-Pareto quantile function models
Yuzhi Cai
title_short Polynomial power-Pareto quantile function models
title_full Polynomial power-Pareto quantile function models
title_fullStr Polynomial power-Pareto quantile function models
title_full_unstemmed Polynomial power-Pareto quantile function models
title_sort Polynomial power-Pareto quantile function models
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
format Journal article
container_title Extremes
container_volume 13
container_issue 3
container_start_page 291
publishDate 2010
institution Swansea University
issn 1386-1999
doi_str_mv 10.1007/s10687-009-0089-3
publisher Springer
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
document_store_str 0
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description In this paper we propose a polynomial power-Pareto quantile function model and a Bayesian method for parameters estimation. We also carried out simulation studies and applied our methodology to real data sets empirically. The results show that a quantile function approach to statistical modelling is very flexible due to the properties of quantile functions, and that the combination of a power and a Pareto distribution enables us to model both the main body and the tails of a distribution, even though the mathematical form of the distribution does not exist. Our research also suggests a new approach to studying extreme values based on a whole data set rather than group maximum/minimum or exceedances above/below a proper threshold value.
published_date 2010-12-31T18:13:51Z
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score 11.04748

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