An exponentiated XLindley distribution with properties, inference and applications.

Publication date: Feb 15, 2024

In this paper, we propose exponentiated XLindley (EXL) distribution. The novel model is adaptable due to the mixt shapes of its density and failure rate functions. The following key statistical properties of EXL distribution are derived: quantile function, moments, hazard function, mean residual life, and RcE9nyi entropy. The parameters are estimated using the maximum likelihood, Anderson Darling, Cramer von Misses, maximum product spacing, ordinary and weighted least square estimation procedures. To examine the behavior of the estimate, Monte Carlo simulation is used. Further Bayesian technique is also utilized to estimate the EXL parameters. The traceplot and Geweke diagnostics are used to track the convergence of simulated processes. The applicability of the EXL distribution is demonstrated by three datasets from different domains such as mortality rate due to COVID-19, precipitation in inches, and failure time for repairable items. The proposed distribution provides efficient results as compared to renowned competitive distributions.

Concepts Keywords
Bayesian Bayesian
Covid Data analysis
Diagnostics Generalization
Inches Inference
Mortality Reliability measures
XLindley distribution

Semantics

Type Source Name
disease MESH COVID-19
disease VO time
disease VO efficient

Original Article

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