A simulation study is performed to assess the maximum likelihood estimations of introduced distribution parameters in terms of bias and mean square error, estimated average length and coverage probability.
📌 3 Key Takeaways
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Objective & Hypothesis:We introduce a four-parameter distribution, called odd Burr power Lindley distribution, which extends the Lindley distribution and has increasing, upside-down and bathtub shapes for the hazard rate function.
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Key Findings & Data:Our purpose is to provide a generalization that may be useful to still more complex situations.
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Conclusion & Impact:A simulation study is performed to assess the maximum likelihood estimations of introduced distribution parameters in terms of bias and mean square error, estimated average length and coverage probability.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:maximum likelihood estimation · simulation · quantile function
Abstract
We introduce a four-parameter distribution, called odd Burr power Lindley distribution, which extends the Lindley distribution and has increasing, upside-down and bathtub shapes for the hazard rate function. Our purpose is to provide a generalization that may be useful to still more complex situations. It includes as special sub-models some well-known distributions such as Lindley, power Lindley, odd log-logistic Lindley, among others. Several statistical properties of the distribution are explored. A simulation study is performed to assess the maximum likelihood estimations of introduced distribution parameters in terms of bias and mean square error, estimated average length and coverage probability.
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