Bayesian extreme rainfall analysis using informative prior

A case study of alor setar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.

Original languageEnglish
Title of host publication2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium
EditorsZahari Ibrahim, Haja Maideen Kader Maideen, Nazlina Ibrahim, Nurul Huda Abd Karim, Taufik Yusof, Fatimah Abdul Razak, Nurulkamal Maseran, Rozida Mohd Khalid, Noor Baa'yah Ibrahim, Hasidah Mohd. Sidek, Mohd Salmi Md Noorani, Norbert Simon
PublisherAmerican Institute of Physics Inc.
Pages913-917
Number of pages5
ISBN (Electronic)9780735412507
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2014 - Selangor, Malaysia
Duration: 9 Apr 201411 Apr 2014

Publication series

NameAIP Conference Proceedings
Volume1614
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

Other2014 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2014
CountryMalaysia
CitySelangor
Period9/4/1411/4/14

Fingerprint

rain gages
Markov chains
inference
stations
inclusions
simulation

Keywords

  • Bayesian mcmc
  • Extreme rainfall analysis
  • Informative priors

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Eli, A., Wan Zin @ Wan Ibrahim, W. Z., Ibrahim, K., & Jemain, A. A. (2014). Bayesian extreme rainfall analysis using informative prior: A case study of alor setar. In Z. Ibrahim, H. M. K. Maideen, N. Ibrahim, N. H. A. Karim, T. Yusof, F. A. Razak, N. Maseran, R. M. Khalid, N. B. Ibrahim, H. M. Sidek, M. S. M. Noorani, ... N. Simon (Eds.), 2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium (pp. 913-917). (AIP Conference Proceedings; Vol. 1614). American Institute of Physics Inc.. https://doi.org/10.1063/1.4895323

Bayesian extreme rainfall analysis using informative prior : A case study of alor setar. / Eli, Annazirin; Wan Zin @ Wan Ibrahim, Wan Zawiah; Ibrahim, Kamarulzaman; Jemain, Abdul Aziz.

2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium. ed. / Zahari Ibrahim; Haja Maideen Kader Maideen; Nazlina Ibrahim; Nurul Huda Abd Karim; Taufik Yusof; Fatimah Abdul Razak; Nurulkamal Maseran; Rozida Mohd Khalid; Noor Baa'yah Ibrahim; Hasidah Mohd. Sidek; Mohd Salmi Md Noorani; Norbert Simon. American Institute of Physics Inc., 2014. p. 913-917 (AIP Conference Proceedings; Vol. 1614).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Eli, A, Wan Zin @ Wan Ibrahim, WZ, Ibrahim, K & Jemain, AA 2014, Bayesian extreme rainfall analysis using informative prior: A case study of alor setar. in Z Ibrahim, HMK Maideen, N Ibrahim, NHA Karim, T Yusof, FA Razak, N Maseran, RM Khalid, NB Ibrahim, HM Sidek, MSM Noorani & N Simon (eds), 2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium. AIP Conference Proceedings, vol. 1614, American Institute of Physics Inc., pp. 913-917, 2014 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2014, Selangor, Malaysia, 9/4/14. https://doi.org/10.1063/1.4895323
Eli A, Wan Zin @ Wan Ibrahim WZ, Ibrahim K, Jemain AA. Bayesian extreme rainfall analysis using informative prior: A case study of alor setar. In Ibrahim Z, Maideen HMK, Ibrahim N, Karim NHA, Yusof T, Razak FA, Maseran N, Khalid RM, Ibrahim NB, Sidek HM, Noorani MSM, Simon N, editors, 2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium. American Institute of Physics Inc. 2014. p. 913-917. (AIP Conference Proceedings). https://doi.org/10.1063/1.4895323
Eli, Annazirin ; Wan Zin @ Wan Ibrahim, Wan Zawiah ; Ibrahim, Kamarulzaman ; Jemain, Abdul Aziz. / Bayesian extreme rainfall analysis using informative prior : A case study of alor setar. 2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium. editor / Zahari Ibrahim ; Haja Maideen Kader Maideen ; Nazlina Ibrahim ; Nurul Huda Abd Karim ; Taufik Yusof ; Fatimah Abdul Razak ; Nurulkamal Maseran ; Rozida Mohd Khalid ; Noor Baa'yah Ibrahim ; Hasidah Mohd. Sidek ; Mohd Salmi Md Noorani ; Norbert Simon. American Institute of Physics Inc., 2014. pp. 913-917 (AIP Conference Proceedings).
@inproceedings{71ddf47ab7444906ab1d778f8504cdfc,
title = "Bayesian extreme rainfall analysis using informative prior: A case study of alor setar",
abstract = "Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.",
keywords = "Bayesian mcmc, Extreme rainfall analysis, Informative priors",
author = "Annazirin Eli and {Wan Zin @ Wan Ibrahim}, {Wan Zawiah} and Kamarulzaman Ibrahim and Jemain, {Abdul Aziz}",
year = "2014",
month = "1",
day = "1",
doi = "10.1063/1.4895323",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
pages = "913--917",
editor = "Zahari Ibrahim and Maideen, {Haja Maideen Kader} and Nazlina Ibrahim and Karim, {Nurul Huda Abd} and Taufik Yusof and Razak, {Fatimah Abdul} and Nurulkamal Maseran and Khalid, {Rozida Mohd} and Ibrahim, {Noor Baa'yah} and Sidek, {Hasidah Mohd.} and Noorani, {Mohd Salmi Md} and Norbert Simon",
booktitle = "2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium",

}

TY - GEN

T1 - Bayesian extreme rainfall analysis using informative prior

T2 - A case study of alor setar

AU - Eli, Annazirin

AU - Wan Zin @ Wan Ibrahim, Wan Zawiah

AU - Ibrahim, Kamarulzaman

AU - Jemain, Abdul Aziz

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.

AB - Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.

KW - Bayesian mcmc

KW - Extreme rainfall analysis

KW - Informative priors

UR - http://www.scopus.com/inward/record.url?scp=85063853541&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063853541&partnerID=8YFLogxK

U2 - 10.1063/1.4895323

DO - 10.1063/1.4895323

M3 - Conference contribution

T3 - AIP Conference Proceedings

SP - 913

EP - 917

BT - 2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium

A2 - Ibrahim, Zahari

A2 - Maideen, Haja Maideen Kader

A2 - Ibrahim, Nazlina

A2 - Karim, Nurul Huda Abd

A2 - Yusof, Taufik

A2 - Razak, Fatimah Abdul

A2 - Maseran, Nurulkamal

A2 - Khalid, Rozida Mohd

A2 - Ibrahim, Noor Baa'yah

A2 - Sidek, Hasidah Mohd.

A2 - Noorani, Mohd Salmi Md

A2 - Simon, Norbert

PB - American Institute of Physics Inc.

ER -