Exponentiated-exponential geometric regression model

Felix Famoye; Carl Lee

doi:10.1080/02664763.2016.1267117

Exponentiated-exponential geometric regression model

Felix Famoye, Carl Lee

Statistics, Actuarial & Data Sciences

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

9 Scopus citations

Abstract

A regression model, based on the exponentiated-exponential geometric distribution, is defined and studied. The regression model can be applied to count data with under-dispersion or over-dispersion. Some forms of its modifications to truncated or inflated data are mentioned. Some tests to discriminate between the regression model and its competitors are discussed. Real numerical data sets are used to illustrate the applications of the regression model.

Original language	English
Pages (from-to)	2963-2977
Number of pages	15
Journal	Journal of Applied Statistics
Volume	44
Issue number	16
DOIs	https://doi.org/10.1080/02664763.2016.1267117
State	Published - Dec 10 2017

Keywords

Count model
tests
under- and over-dispersion
zero-inflation

Access to Document

10.1080/02664763.2016.1267117

Cite this

@article{6c25d5630eb34121b97b8f4e205d372d,

title = "Exponentiated-exponential geometric regression model",

abstract = "A regression model, based on the exponentiated-exponential geometric distribution, is defined and studied. The regression model can be applied to count data with under-dispersion or over-dispersion. Some forms of its modifications to truncated or inflated data are mentioned. Some tests to discriminate between the regression model and its competitors are discussed. Real numerical data sets are used to illustrate the applications of the regression model.",

keywords = "Count model, tests, under- and over-dispersion, zero-inflation",

author = "Felix Famoye and Carl Lee",

note = "Publisher Copyright: {\textcopyright} 2016 Informa UK Limited, trading as Taylor & Francis Group.",

year = "2017",

month = dec,

day = "10",

doi = "10.1080/02664763.2016.1267117",

language = "English",

volume = "44",

pages = "2963--2977",

journal = "Journal of Applied Statistics",

issn = "0266-4763",

publisher = "Journal of Applied Statistics",

number = "16",

}

TY - JOUR

T1 - Exponentiated-exponential geometric regression model

AU - Famoye, Felix

AU - Lee, Carl

PY - 2017/12/10

Y1 - 2017/12/10

N2 - A regression model, based on the exponentiated-exponential geometric distribution, is defined and studied. The regression model can be applied to count data with under-dispersion or over-dispersion. Some forms of its modifications to truncated or inflated data are mentioned. Some tests to discriminate between the regression model and its competitors are discussed. Real numerical data sets are used to illustrate the applications of the regression model.

AB - A regression model, based on the exponentiated-exponential geometric distribution, is defined and studied. The regression model can be applied to count data with under-dispersion or over-dispersion. Some forms of its modifications to truncated or inflated data are mentioned. Some tests to discriminate between the regression model and its competitors are discussed. Real numerical data sets are used to illustrate the applications of the regression model.

KW - Count model

KW - tests

KW - under- and over-dispersion

KW - zero-inflation

UR - http://www.scopus.com/inward/record.url?scp=85006173186&partnerID=8YFLogxK

U2 - 10.1080/02664763.2016.1267117

DO - 10.1080/02664763.2016.1267117

M3 - Article

AN - SCOPUS:85006173186

VL - 44

SP - 2963

EP - 2977

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

SN - 0266-4763

IS - 16

ER -

Exponentiated-exponential geometric regression model

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this