Past extropy for linear consecutive r-out-of-n: F systems and its properties, bounds, and estimation

Faten Alrewely; Mohamed Kayid; Boshra Alarfaj; Narayanaswamy Balakrishnan; Faten Alrewely; Mohamed Kayid; Boshra Alarfaj; Narayanaswamy Balakrishnan

doi:10.3934/math.2026427

AIMS Mathematics

2026, Volume 11, Issue 4: 10342-10371. doi: 10.3934/math.2026427

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Past extropy for linear consecutive r-out-of-n: F systems and its properties, bounds, and estimation

1.
Department of Mathematics, College of Science, Jouf University, P.O. Box 2014, Sakaka 72388, Saudi Arabia
2.
Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
3.
Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada
4.
Department of Mathematics, Atılım University, Ankara, Turkey

Received: 25 January 2026 Revised: 26 March 2026 Accepted: 02 April 2026 Published: 15 April 2026
MSC : 62G10, 62N05, 94A17

This paper develops a rigorous information-theoretic framework for uncertainty quantification in lifetime analysis through the concept of past extropy. Particular attention is paid to linear consecutive $ r $-out-of- $ n $: $ F $ systems with independent and identically distributed component lifetimes. Explicit analytical expressions for the past extropy of system lifetimes are derived, and several new theoretical properties are established, including monotonicity results, probabilistic bounds, and characterization theorems, which reveal structural relationships between system reliability configurations and information-based uncertainty measures. The investigation is further extended to conditional past extropy, providing additional insight into uncertainty assessment under partial system information. To support practical implementation, a nonparametric kernel-based estimator of past extropy is proposed. Its performance is evaluated through Monte Carlo simulation experiments that illustrate stable finite-sample behavior and reliable estimation accuracy across different parameter settings. The presented results demonstrate that past extropy offers a flexible and informative framework for studying uncertainty in lifetime distributions and structured reliability systems, combining rigorous theoretical developments with practical data-driven estimation methodology.
- past extropy,
- reliability modeling,
- stochastic orders,
- characterization,
- nonparametric estimation
Citation: Faten Alrewely, Mohamed Kayid, Boshra Alarfaj, Narayanaswamy Balakrishnan. Past extropy for linear consecutive r-out-of-n: F systems and its properties, bounds, and estimation[J]. AIMS Mathematics, 2026, 11(4): 10342-10371. doi: 10.3934/math.2026427

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Abstract

This paper develops a rigorous information-theoretic framework for uncertainty quantification in lifetime analysis through the concept of past extropy. Particular attention is paid to linear consecutive $ r $-out-of- $ n $: $ F $ systems with independent and identically distributed component lifetimes. Explicit analytical expressions for the past extropy of system lifetimes are derived, and several new theoretical properties are established, including monotonicity results, probabilistic bounds, and characterization theorems, which reveal structural relationships between system reliability configurations and information-based uncertainty measures. The investigation is further extended to conditional past extropy, providing additional insight into uncertainty assessment under partial system information. To support practical implementation, a nonparametric kernel-based estimator of past extropy is proposed. Its performance is evaluated through Monte Carlo simulation experiments that illustrate stable finite-sample behavior and reliable estimation accuracy across different parameter settings. The presented results demonstrate that past extropy offers a flexible and informative framework for studying uncertainty in lifetime distributions and structured reliability systems, combining rigorous theoretical developments with practical data-driven estimation methodology.

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