Abstract
In this paper, we introduce a generalization of the two-parameter double Lindley distribution (TPDLD) of Kumar and Jose [C. S. Kumar and R. Jose, A new generalization to Laplace distribution, J. Math. Comput. 31 2020, 8–32], namely the generalized double Lindley distribution (GDLD) along with its location-scale extension (EGDLD). Then we discuss the estimation of parameters of the EGDLD by the maximum likelihood estimation procedure. Next, we illustrate this estimation procedure with the help of certain real life data sets, and a simulation study is carried out to examine the performance of various estimators of the parameters of the distribution.
Acknowledgements
The authors wish to express their sincere gratitude to the editor in chief and anonymous referees for their valuable comments on an earlier version of the manuscript that greatly improved the quality of its presentation.
References
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Articles in the same Issue
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- A Schrödinger random operator with semimartingale potential
- Fractional neutral functional differential equations driven by the Rosenblatt process with an infinite delay
- Modified information criterion for detecting changes in skew slash distribution
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Articles in the same Issue
- Frontmatter
- Generalized backward stochastic differential equations with jumps in a general filtration
- A Schrödinger random operator with semimartingale potential
- Fractional neutral functional differential equations driven by the Rosenblatt process with an infinite delay
- Modified information criterion for detecting changes in skew slash distribution
- Stability results for stochastic differential equations driven by an additive fractional Brownian sheet
- Delay BSDEs driven by fractional Brownian motion
- Generalized double Lindley distribution: A new model for weather and financial data