DOAJ Open Access 2021

Kernel Estimation of Cumulative Residual Tsallis Entropy and Its Dynamic Version under <i>ρ</i>-Mixing Dependent Data

Muhammed Rasheed Irshad Radhakumari Maya Francesco Buono Maria Longobardi

Abstrak

Tsallis introduced a non-logarithmic generalization of Shannon entropy, namely Tsallis entropy, which is non-extensive. Sati and Gupta proposed cumulative residual information based on this non-extensive entropy measure, namely cumulative residual Tsallis entropy (CRTE), and its dynamic version, namely dynamic cumulative residual Tsallis entropy (DCRTE). In the present paper, we propose non-parametric kernel type estimators for CRTE and DCRTE where the considered observations exhibit an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula>-mixing dependence condition. Asymptotic properties of the estimators were established under suitable regularity conditions. A numerical evaluation of the proposed estimator is exhibited and a Monte Carlo simulation study was carried out.

Topik & Kata Kunci

Science Astrophysics Physics

Penulis (4)

Muhammed Rasheed Irshad

Radhakumari Maya

Francesco Buono

Maria Longobardi

Format Sitasi

APA MLA BibTeX

Irshad, M.R., Maya, R., Buono, F., Longobardi, M. (2021). Kernel Estimation of Cumulative Residual Tsallis Entropy and Its Dynamic Version under <i>ρ</i>-Mixing Dependent Data. https://doi.org/10.3390/e24010009

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Informasi Jurnal

Tahun Terbit: 2021
Sumber Database: DOAJ
DOI: 10.3390/e24010009
Akses: Open Access ✓