Abstract
In this paper, Mathai–Haubold past entropy measure is proposed and its properties are studied. Also some generalized inequalities related to Mathai–Haubold entropy measure are discussed. A Kernel based non-parametric estimator for the proposed measure is provided when the underlying sample follows \(\rho\)-mixing dependence condition. The consistency property and asymptotic normality of the proposed estimator are established. A simulation study is conducted to assess the performance of the estimator. A data set is analyzed for illustrative purposes.
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Das, O., Chakraborty, S. & Pradhan, B. On Mathai–Haubold Past Entropy Measure. J Indian Soc Probab Stat (2024). https://doi.org/10.1007/s41096-024-00183-y
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DOI: https://doi.org/10.1007/s41096-024-00183-y
Keywords
- Mathai–Haubold entropy
- Log-sum inequality
- \(\rho\)-mixing dependence condition
- Kernel function
- Asymptotic normality