Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Novel Version Three Parameter Nadarajah Haghighi Model: Entropy Measures, Inference and Applications

Version 1 : Received: 17 April 2024 / Approved: 17 April 2024 / Online: 18 April 2024 (13:49:06 CEST)

A peer-reviewed article of this Preprint also exists.

Alshawarbeh, E.; Alghamdi, F.M.; Meraou, M.A.; Aljohani, H.M.; Abdelraouf, M.; Riad, F.H.; Alsheikh, S.M.A.; Alsolmi, M.M. A Novel Three-Parameter Nadarajah Haghighi Model: Entropy Measures, Inference, and Applications. Symmetry 2024, 16, 751. Alshawarbeh, E.; Alghamdi, F.M.; Meraou, M.A.; Aljohani, H.M.; Abdelraouf, M.; Riad, F.H.; Alsheikh, S.M.A.; Alsolmi, M.M. A Novel Three-Parameter Nadarajah Haghighi Model: Entropy Measures, Inference, and Applications. Symmetry 2024, 16, 751.

Abstract

The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences, notably lifetime, medical, and financial sciences. The Heavy tailed Nadarajah Haghighi model is introduced by compounding the heavy tailed family and Nadarajah Haghighi distribution in this paper. The so obtained model has three parameters accounting for the scale and shape of the distribution. The proposed distribution's fundamental characteristics such as probability density, cumulative distribution, hazard rate, and survival functions are provided, and several key statistical properties are established, and several entropy information measures are proposed. The estimation of model parameter is performed via the ML procedure. Further, different simulation experiments are conducted to demonstrate the performance of proposed estimator’s using some measure, like the average estimate, the average bias, and associated mean square error. Finally, we are applied our proposed model to analyze three different real datasets. In our illustration, we are compared the practicality of the recommended model with several well-known competing models.

Keywords

Asymmetric dataset; Heavy tailed; MLE procedure; Nadarajah Haghighi; Simulation experiments; Survival function; Symmetric dataset

Subject

Computer Science and Mathematics, Probability and Statistics

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