Unit distributions, exhibiting inherent symmetrical properties, have been extensively studied
across various fields. A significant challenge in these studies, particularly evident in …

This study examined the performance of random forest (RF), support vector machine (SVM)
and adaptive boosting (AB) machine learning models used to estimate daily potato crop …

This paper introduces the Poisson-Lindley minification integer-valued autoregressive (PLMINAR)
process, a novel statistical model for analyzing count time series data. The modified …

Financial markets are increasingly interlinked. Therefore, this study explores the complex
relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) …

The direct estimation of soil hydraulic conductivity (Ks) requires expensive laboratory
measurement to present adequately soil properties in an area of interest. Moreover, the estimation …

In the vast statistical literature, there are numerous probability distribution models that can
model data from real-world phenomena. New probability models, nevertheless, are still …

This paper explores bimodal skew-symmetric distributions, a versatile family of distributions
characterized by parameters that control asymmetry and kurtosis. These distributions …

Count data consists of both observed and unobserved events. The analysis of count data
often encounters overdispersion, where traditional Poisson models may not be adequate. In …

One of the issues that researchers may encounter in count data is having many zeros. One
of the solutions to model these data is using zero-inflated Poisson (ZIP) regression models. …

Excessive zeros in count data pose challenges in statistical modeling, particularly in insurance
applications. Zero-inflated (ZI) and hurdle models are commonly employed to address …