Performance evaluation of estimators in the presence of outliers or omitted predictors: A study on the Poisson-Exponential regression model
Yazarlar (2)
Doç. Dr. Yasin ALTINIŞIK Sinop Üniversitesi, Türkiye
Doç. Dr. Demet HAN AYDIN Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Communications in Statistics Simulation and Computation (Q3) | ||
| Dergi ISSN | 0361-0918 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2025 |
| Kabul Tarihi | 05-10-2023 | Yayınlanma Tarihi | 30-10-2023 |
| Cilt / Sayı / Sayfa | 54 / 4 / 1038–1075 | DOI | 10.1080/03610918.2023.2270184 |
| Makale Linki | https://www.tandfonline.com/doi/full/10.1080/03610918.2023.2270184 | ||
| UAK Araştırma Alanları |
İstatistiksel Analiz
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| Özet |
| The Poisson regression model is vulnerable for overdispersion in the data when estimating model parameters and their standard errors. Overdispersion may occur due to outliers in the data and/or removal of important predictors from the model. This paper employs a regression model that can be used to better cope with outliers and omitted predictor bias in count data compared to the Poisson regression model, namely the Poisson exponential (PE) model which is a reparameterization of the geometric regression model. Along with investigating the distributional properties of the PE distribution, the usual maximum likelihood (ML-I), maximum likelihood with Expectation-maximization algorithm (ML-II), least-square (LS), weighted least-square (WLS), least-absolute deviation (LAD), weighted least-absolute deviation (WLAD), and Cramer-von mises (CVM) estimation methods are utilized in the context of the PE … |
| Anahtar Kelimeler |
| Count data | Omitted predictors | Outliers | Overdispersion | Poisson exponential model |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 1 |