One-inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin

Gul Inan

doi:10.1002/bimj.201700261

One-inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin

Gul Inan^*

^*Bu çalışma için yazışmadan sorumlu yazar

Middle East Technical University

Araştırma sonucu: Dergiye katkı › Mektup › bilirkişi

1 Atıf (Scopus)

Özet

In this study, we would like to show that the one-inflated zero-truncated negative binomial (OIZTNB) regression model can be easily implemented in R via built-in functions when we use mean-parameterization feature of negative binomial distribution to build OIZTNB regression model. From the practitioners' point of view, we believe that this approach presents a computationally convenient way for implementation of the OIZTNB regression model.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	859-864
Sayfa sayısı	6
Dergi	Biometrical Journal
Hacim	60
Basın numarası	4
DOI'lar	https://doi.org/10.1002/bimj.201700261
Yayın durumu	Yayınlandı - Tem 2018
Harici olarak yayınlandı	Evet

Bibliyografik not

Publisher Copyright:
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Belgeye Erişim

10.1002/bimj.201700261

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@article{fdc73e6045e14a558b6f244abc4b8933,

title = "One-inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin",

abstract = "In this study, we would like to show that the one-inflated zero-truncated negative binomial (OIZTNB) regression model can be easily implemented in R via built-in functions when we use mean-parameterization feature of negative binomial distribution to build OIZTNB regression model. From the practitioners' point of view, we believe that this approach presents a computationally convenient way for implementation of the OIZTNB regression model.",

keywords = "count data, mean parameterization, one-inflation, zero-truncation",

author = "Gul Inan",

note = "Publisher Copyright: {\textcopyright} 2018 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim",

year = "2018",

month = jul,

doi = "10.1002/bimj.201700261",

language = "English",

volume = "60",

pages = "859--864",

journal = "Biometrical Journal",

issn = "0323-3847",

publisher = "Wiley-VCH Verlag",

number = "4",

}

TY - JOUR

T1 - One-inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin

AU - Inan, Gul

PY - 2018/7

Y1 - 2018/7

N2 - In this study, we would like to show that the one-inflated zero-truncated negative binomial (OIZTNB) regression model can be easily implemented in R via built-in functions when we use mean-parameterization feature of negative binomial distribution to build OIZTNB regression model. From the practitioners' point of view, we believe that this approach presents a computationally convenient way for implementation of the OIZTNB regression model.

AB - In this study, we would like to show that the one-inflated zero-truncated negative binomial (OIZTNB) regression model can be easily implemented in R via built-in functions when we use mean-parameterization feature of negative binomial distribution to build OIZTNB regression model. From the practitioners' point of view, we believe that this approach presents a computationally convenient way for implementation of the OIZTNB regression model.

KW - count data

KW - mean parameterization

KW - one-inflation

KW - zero-truncation

UR - https://www.scopus.com/pages/publications/85046736835

U2 - 10.1002/bimj.201700261

DO - 10.1002/bimj.201700261

M3 - Letter

C2 - 29749022

AN - SCOPUS:85046736835

SN - 0323-3847

VL - 60

SP - 859

EP - 864

JO - Biometrical Journal

JF - Biometrical Journal

IS - 4

ER -

One-inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin

Özet

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap