A Generalization of the Quantile-Based Flattened Logistic Distribution
| Autor: | Tapan Kumar Chakrabarty, Dreamlee Sharma |
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| Rok vydání: | 2021 |
| Předmět: |
Matching (statistics)
Logistic distribution Distribution (number theory) Generalization 020209 energy 02 engineering and technology 01 natural sciences Measure (mathematics) Computer Science Applications 010104 statistics & probability Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Kurtosis Business Management and Accounting (miscellaneous) Applied mathematics 0101 mathematics Statistics Probability and Uncertainty Closed-form expression Quantile Mathematics |
| Zdroj: | Annals of Data Science. 8:603-627 |
| ISSN: | 2198-5812 2198-5804 0361-0926 |
| Popis: | In this paper, we propose a generalization of the quantile-based flattened logistic distribution Sharma and Chakrabarty (Commun Stat Theory Methods 48(14):3643–3662, 2019. https://doi.org/10.1080/03610926.2018.1481966 ). Having described the need for such a generalization from the data science perspective, several important properties of the distribution are derived here. We show that the rth order L-moment of the distribution can be written in a closed form expression. The L-skewness ratio and the L-kurtosis ratio of the distribution have been studied in detail. The distribution is shown to posses a skewness-invariant kurtosis measure based on quantiles and L-moments. The method of matching L-moments estimation has been used to estimate the parameters of the proposed model. The model has been applied to two real-life datasets and appropriate goodness-of-fit procedures have been used to test the validity of the model. |
| Databáze: | OpenAIRE |
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