New zero-inflated regression models with a variant of censoring
Yazarlar (2)
Doç. Dr. Yasin ALTINIŞIK Sinop Üniversitesi, Türkiye
Prof. Dr. Emel ÇANKAYA Sinop Üniversitesi, Türkiye
Makale Türü Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı Brazilian Journal of Probability and Statistics (Q3)
Dergi ISSN 0103-0752 Dergi Bilgileri (2022)
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce Basım Tarihi 12-2022
Kabul Tarihi – Yayınlanma Tarihi 01-12-2022
Cilt / Sayı / Sayfa 36 / 4 / 641–674 DOI 10.1214/22-bjps544
Makale Linki https://projecteuclid.org/journals/brazilian-journal-of-probability-and-statistics/volume-36/issue-4/New-zero-inflated-regression-models-with-a-variant-of-censoring/10.1214/22-BJPS544.full
UAK Araştırma Alanları
İstatistiksel Analiz Uygulamalı İstatistik Teorik İstatistik
Özet
There is ever growing demand of modeling overdispersed count data generated by various disiplines. Excessive number of zeros and hetero-geneity in the population are two main sources of the overdispersion problem. Development of new count models that are more flexible than conventional Poisson model is thus necessary in order to address such sources. This study fullfils this need by proposing a new heterogeneous Poisson model with a cap-ture of excess zeros, namely zero-inflated Poisson–Ailamujia (ZIPA) model. In line with the aim of curing overdispersion, a censored variant of this newly suggested model is also here developed. An extensive simulation study is conducted to assess the performances of both forms of new models in terms of bias, precision and accuracy measures. Additionally, two real world applications are presented to illustrate practical implications of zero-inflated (censored) Poisson–Ailamujia models in comparison to some alternatives.
Anahtar Kelimeler
censoring | compound Poisson | count regression | Lifetime datasets | overdispersion | Poisson–Ailamujia model | zero-inflation
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Scopus 3
New zero-inflated regression models with a variant of censoring