Reliability and estimation of the zero-inflated transmuted geometric distribution with applications and actuarial insights

Kalpasree Sharma (), Partha Jyoti Hazarika, Mohamed S. Eliwa and Mahmoud El-Morshedy
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Kalpasree Sharma: Dibrugarh University, Department of Statistics
Partha Jyoti Hazarika: Dibrugarh University, Department of Statistics
Mohamed S. Eliwa: Qassim University, Department of Statistics and Operations Research, College of Science
Mahmoud El-Morshedy: Prince Sattam bin Abdulaziz University, Department of Mathematics, College of Science and Humanities in Al-Kharj

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2026, vol. 32, issue 1, No 1, 29 pages

Abstract: Abstract Overdispersion is a phenomenon which is quite common in many real-life count data sets and these variability often results due to an excessive number of zeros. To address this issue, zero-inflated distributions provide a flexible modeling approach capable of capturing high levels of dispersion. In this paper we introduce a new count distribution known as the zero-inflated transmuted geometric distribution. We explore its key statistical properties, reliability aspects and actuarial traits. Additionally we employ different estimation strategies and conduct a simulation study to assess the performance of the estimators. We demonstrate the practical utility of the proposed model through the analysis of three empirical data sets. Lastly, we also carry out the likelihood ratio test to justify the use of the proposed zero-inflated distribution.

Keywords: Statistical model; Discrete distribution; Zero-inflated model; Overdispersion; Geometric distribution; Reliability theory; Estimation methods; Simulation (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/s10985-025-09683-w

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