2017/09/17 by S. Chakraborty, Subrata Chakraborty, S. H. Ong +5
Decision Sciences · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Mathematics #Probability and Risk Models #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference #Statistics Theory (math.ST) #math.ST #stat.AP #stat.TH
paper · pdf · doi:10.48550/arxiv.1709.05613
37 pages; 2 Figures, 3 Table, Pre-print Version-4.0
openalex publication_date 2017/09/17 · arxiv created 2020/02/06 · arxiv updated 2020/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A new distribution on (0, 1), generalized Log-Lindley distribution, is proposed by extending the Log-Lindley distribution. This new distribution is shown to be a weighted Log-Lindley distribution. Important probabilistic and statistical properties have been derived. An interesting characterization of the weighted distribution in terms of Kullback-Liebler distance and weighted entropy has also been obtained. A useful result in insurance for the distorted premium principal is presented and verified with numerical calculations. New regression models for bounded responses based on this distribution and their application is illustrated by considering modeling a real life data on risk management and another data set on outpatient health expenditure in comparison with beta regression and Log-Lindley regression models. Much better fits for both data sets justify the relevance of the new distribution in statistical modeling and analysis. Furthermore this generalization, apart from adding flexibility for modelling, retains the compactness and tractability of statistical quantities required for statistical analysis, which is a feature of the Log-Lindley distribution. Thus, the generalized Log-Lindley distribution should be a useful addition to statistical models for practitioners.