Non-parametric estimation of the generalized past entropy function under α -mixing sample

Probability in the Engineering and Informational Sciences, vol. 40, pp. 272–287

Abstract

Abstract Measure of uncertainty in past lifetime distribution plays an important role in the context of information theory, forensic science and other related fields. In the present work, we propose non-parametric kernel type estimator for generalized past entropy function, which was introduced by Gupta and Nanda [9], under $\alpha$ -mixing sample. The resulting estimator is shown to be weak and strong consistent and asymptotically normally distributed under certain regularity conditions. The performance of the estimator is validated through simulation study and a real data set.

Authors 4

1. Radhakumari Maya

   Cochin University of Science and Technology

   Affiliation as printed

   Cochin University of Science and Technology

2. M. R. Irshad

   Cochin University of Science and Technology

   Affiliation as printed

   Cochin University of Science and Technology

3. Francesco Buono Aachen Institute of Statistics

   RWTH Aachen University

   Affiliation as printed

   Institute of Statistics, RWTH Aachen University

4. Maria Longobardi corresponding

   University of Naples Federico II

   Affiliation as printed

   Università degli Studi di Napoli Federico II