Dependence models arising from the lagrangian probability distributions

Shubiao Li; Dennis Black; Carl Lee; Felix Famoye; Sung Li

doi:10.1080/03610920902887715

Dependence models arising from the lagrangian probability distributions

Shubiao Li, Dennis Black, Carl Lee, Felix Famoye, Sung Li

Statistics, Actuarial & Data Sciences

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

4 Scopus citations

Abstract

In this article, we propose a method based on the Lagrangian probability distributions for developing new dependence models. Specifically, a generalized Poisson-gamma dependence model is derived. The maximum likelihood estimation (MLE) technique is proposed for estimating the dependence model parameters. Application of the generalized Poisson-gamma dependence model is illustrated by using an operational risks dataset.

Original language	English
Pages (from-to)	1729-1742
Number of pages	14
Journal	Communications in Statistics - Theory and Methods
Volume	39
Issue number	10
DOIs	https://doi.org/10.1080/03610920902887715
State	Published - Jun 2010

Keywords

Conditional independence
Discrete model
Estimation
Operational risk

Access to Document

10.1080/03610920902887715

Cite this

@article{129a241c235d4e7c99b19f124f5846d2,

title = "Dependence models arising from the lagrangian probability distributions",

abstract = "In this article, we propose a method based on the Lagrangian probability distributions for developing new dependence models. Specifically, a generalized Poisson-gamma dependence model is derived. The maximum likelihood estimation (MLE) technique is proposed for estimating the dependence model parameters. Application of the generalized Poisson-gamma dependence model is illustrated by using an operational risks dataset.",

keywords = "Conditional independence, Discrete model, Estimation, Operational risk",

author = "Shubiao Li and Dennis Black and Carl Lee and Felix Famoye and Sung Li",

year = "2010",

month = jun,

doi = "10.1080/03610920902887715",

language = "English",

volume = "39",

pages = "1729--1742",

journal = "Communications in Statistics - Theory and Methods",

issn = "0361-0926",

publisher = "Communications in Statistics - Theory and Methods",

number = "10",

}

TY - JOUR

T1 - Dependence models arising from the lagrangian probability distributions

AU - Li, Shubiao

AU - Black, Dennis

AU - Lee, Carl

AU - Famoye, Felix

AU - Li, Sung

PY - 2010/6

Y1 - 2010/6

N2 - In this article, we propose a method based on the Lagrangian probability distributions for developing new dependence models. Specifically, a generalized Poisson-gamma dependence model is derived. The maximum likelihood estimation (MLE) technique is proposed for estimating the dependence model parameters. Application of the generalized Poisson-gamma dependence model is illustrated by using an operational risks dataset.

AB - In this article, we propose a method based on the Lagrangian probability distributions for developing new dependence models. Specifically, a generalized Poisson-gamma dependence model is derived. The maximum likelihood estimation (MLE) technique is proposed for estimating the dependence model parameters. Application of the generalized Poisson-gamma dependence model is illustrated by using an operational risks dataset.

KW - Conditional independence

KW - Discrete model

KW - Estimation

KW - Operational risk

UR - http://www.scopus.com/inward/record.url?scp=77952373564&partnerID=8YFLogxK

U2 - 10.1080/03610920902887715

DO - 10.1080/03610920902887715

M3 - Article

AN - SCOPUS:77952373564

VL - 39

SP - 1729

EP - 1742

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 10

ER -

Dependence models arising from the lagrangian probability distributions

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this