Performance of prior and weighting biascorrection methods for rare event logisticregression under the influence of sampling bias
Yazarlar (2)
Dr. Öğr. Üyesi Olcay ALPAY 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 | Communications in Statistics Simulation and Computation (Q3) | ||
| Dergi ISSN | 0361-0918 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2023 |
| Kabul Tarihi | 02-01-2021 | Yayınlanma Tarihi | 31-01-2023 |
| Cilt / Sayı / Sayfa | 52 / 3 / 993–1014 | DOI | 10.1080/03610918.2021.1872629 |
| Makale Linki | https://www.tandfonline.com/doi/pdf/10.1080/03610918.2021.1872629?needAccess=true | ||
| UAK Araştırma Alanları |
Uygulamalı İstatistik
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| Özet |
| The problem of classifying events to binary classes has been popularly addressed by Logistic Regression Analysis. However, there may be situations where the most interested class of event is rare such as an infectious disease, earthquake, financial crisis etc. The model of such events tends to focus on the majority class, resulting in the underestimation of probabilities for the rare class. Additionally, the model may incorporate sampling bias if the rare class of the sample is not representative of its population. It is therefore important to investigate whether such rareness is genuine or caused by an improperly drawn sample. We conducted a simulation study by creating three populations with different rarity levels and drawing samples from each of those which are either compatible or incompatible with the actual rare classes of the population. Then, the effect of sampling bias is discussed under the two correction ... |
| Anahtar Kelimeler |
| Logistic regression | Prior bias correction | Rare event | Sampling bias | Weighting bias correction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |