Asar Y.Ahmed S.E.Yüzbaşı B.2024-08-042024-08-0420192522-5022https://doi.org/10.1007/978-3-319-93351-1_27https://hdl.handle.net/11616/92031When there is an excess amount of zeros and over-dispersion in the dependent variable, the zero-inflated Poisson regression is usually used to model the data. In most of the situations, researchers may encounter near linear dependencies in the exploratory variables which leads to the collinearity problem. Therefore, we propose to use Liu-type estimator to overcome this problem. We compare our method to the well-known ridge estimator via a Monte Carlo simulation study and real data examples. According to the results, our method is a better alternative in the presence of collinearity. © 2019, Springer International Publishing AG, part of Springer Nature.eninfo:eu-repo/semantics/closedAccessCount data modelsIll-conditioned design matrixLiu-type estimatorRidge estimatorZero-inflated poisson regressionBook ChapterPart F4632934210.1007/978-3-319-93351-1_272-s2.0-85161359263N/A