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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Power modified XLindley distribution: Statistical properties and applications.

Yusra A Tashkandy¹, M E Bakr¹, Sid Ahmed Benchiha²

¹Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia.

Scientific Reports

|August 31, 2024

Summary

A new power-modified XLindley distribution offers enhanced statistical modeling flexibility. This novel distribution shows superior performance in fitting real-world flood and reliability data compared to existing models.

Keywords:

Application Estimation methods Modified XLindley distribution Moments

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Area of Science:

Statistics
Probability Theory

Background:

The modified XLindley distribution is a known statistical model.
Enhancements in flexibility and adaptability are crucial for statistical modeling.

Purpose of the Study:

Introduce a novel two-parameter distribution: the power-modified XLindley distribution.
Examine the statistical properties and data fitting capabilities of this new distribution.
Evaluate parameter estimation techniques and demonstrate practical applications.

Main Methods:

Developed the power-modified XLindley distribution using power transformation techniques.
Analyzed statistical properties and conducted simulation experiments for parameter estimation.
Applied the distribution to real-world flood and reliability engineering datasets.

Main Results:

The power-modified XLindley distribution demonstrates enhanced flexibility and adaptability.
The maximum product of the spacings method proved effective for parameter estimation.
The proposed distribution outperformed existing models in fitting flood and reliability data.

Conclusions:

The power-modified XLindley distribution is a valuable addition to statistical modeling tools.
This novel distribution shows significant potential for applications in natural disaster analysis and reliability engineering.
The study highlights the effectiveness of the proposed distribution for real-world data analysis.