Bayesian prediction of rainfall records using the generalized exponential distribution

Mohamed T. Madi; Mohammad Z. Raqab

doi:10.1002/env.826

Bayesian prediction of rainfall records using the generalized exponential distribution

Mohamed T. Madi, Mohammad Z. Raqab

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

The Los Angeles rainfall data are found to fit well to the two-parameter generalized exponential (GE) distribution. A Bayesian parametric approach is described and used to predict the behavior of further rainfall records. Importance sampling is used to estimate the model parameters, and the Gibbs and Metropolis samplers are used to implement the prediction procedure.

Original language	English
Pages (from-to)	541-549
Number of pages	9
Journal	Environmetrics
Volume	18
Issue number	5
DOIs	https://doi.org/10.1002/env.826
Publication status	Published - Aug 2007

Keywords

Bayesian estimation
Bayesian prediction
Generalized exponential distribution
Gibbs and Metropolis sampling
Importance sampling
Record statistics

ASJC Scopus subject areas

Statistics and Probability
Ecological Modelling

Access to Document

10.1002/env.826

Cite this

@article{7c524e92a1e343ebaef80bb5bcf2f5c2,

title = "Bayesian prediction of rainfall records using the generalized exponential distribution",

abstract = "The Los Angeles rainfall data are found to fit well to the two-parameter generalized exponential (GE) distribution. A Bayesian parametric approach is described and used to predict the behavior of further rainfall records. Importance sampling is used to estimate the model parameters, and the Gibbs and Metropolis samplers are used to implement the prediction procedure.",

keywords = "Bayesian estimation, Bayesian prediction, Generalized exponential distribution, Gibbs and Metropolis sampling, Importance sampling, Record statistics",

author = "Madi, {Mohamed T.} and Raqab, {Mohammad Z.}",

year = "2007",

month = aug,

doi = "10.1002/env.826",

language = "English",

volume = "18",

pages = "541--549",

journal = "Environmetrics",

issn = "1180-4009",

publisher = "John Wiley and Sons Ltd",

number = "5",

}

TY - JOUR

T1 - Bayesian prediction of rainfall records using the generalized exponential distribution

AU - Madi, Mohamed T.

AU - Raqab, Mohammad Z.

PY - 2007/8

Y1 - 2007/8

N2 - The Los Angeles rainfall data are found to fit well to the two-parameter generalized exponential (GE) distribution. A Bayesian parametric approach is described and used to predict the behavior of further rainfall records. Importance sampling is used to estimate the model parameters, and the Gibbs and Metropolis samplers are used to implement the prediction procedure.

AB - The Los Angeles rainfall data are found to fit well to the two-parameter generalized exponential (GE) distribution. A Bayesian parametric approach is described and used to predict the behavior of further rainfall records. Importance sampling is used to estimate the model parameters, and the Gibbs and Metropolis samplers are used to implement the prediction procedure.

KW - Bayesian estimation

KW - Bayesian prediction

KW - Generalized exponential distribution

KW - Gibbs and Metropolis sampling

KW - Importance sampling

KW - Record statistics

UR - http://www.scopus.com/inward/record.url?scp=34447537784&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34447537784&partnerID=8YFLogxK

U2 - 10.1002/env.826

DO - 10.1002/env.826

M3 - Article

AN - SCOPUS:34447537784

SN - 1180-4009

VL - 18

SP - 541

EP - 549

JO - Environmetrics

JF - Environmetrics

IS - 5

ER -

Bayesian prediction of rainfall records using the generalized exponential distribution

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this