A GENERALIZED ENTROPY-BASED RESIDUAL LIFETIME DISTRIBUTIONS

2011 ◽  
Vol 04 (02) ◽  
pp. 171-184 ◽  
Author(s):  
VIKAS KUMAR ◽  
H. C. TANEJA
Keyword(s):  
Residual Life ◽  
Survival Function ◽  
Monotonicity Property ◽  
Entropy Measure ◽  
Residual Lifetime ◽  
Mean Residual Life ◽  
Present Communication ◽  
Dynamic Information ◽  
Lifetime Distributions ◽  
Life Function

The present communication considers Havrda and Charvat entropy measure to propose a generalized dynamic information measure. It is shown that the proposed measure determines the survival function uniquely. The residual lifetime distributions have been characterized. A bound for the dynamic entropy measure in terms of mean residual life function has been derived, and its monotonicity property is studied.

scholarly journals Some new results on bathtub-shaped hazard rate models

2021 ◽  
Vol 19 (2) ◽  
pp. 1239-1250
Author(s):  
Mohamed Kayid ◽  
Keyword(s):  
Order Statistics ◽  
Hazard Rate ◽  
Residual Life ◽  
Change Points ◽  
Residual Lifetime ◽  
Lifetime Distributions ◽  
Residual Life Function ◽  
Basic Model ◽  
Quantile Residual Life ◽  
Life Function

<abstract><p>The most common non-monotonic hazard rate situations in life sciences and engineering involves bathtub shapes. This paper focuses on the quantile residual life function in the class of lifetime distributions that have bathtub-shaped hazard rate functions. For this class of distributions, the shape of the $ \alpha $-quantile residual lifetime function was studied. Then, the change points of the $ \alpha $-quantile residual life function of a general weighted hazard rate model were compared with the corresponding change points of the basic model in terms of their location. As a special weighted model, the order statistics were considered and the change points related to the order statistics were compared with the change points of the baseline distribution. Moreover, some comparisons of the change points of two different order statistics were presented.</p></abstract>

Characterizations of Lifetime Distributions Based on Doubly Truncated Mean Residual Life and Mean Past to Failure

2012 ◽  
Vol 41 (6) ◽  
pp. 1105-1115 ◽  
Author(s):  
M. Khorashadizadeh ◽  
A. H. Rezaei Roknabadi ◽  
G. R. Mohtashami Borzadaran
Keyword(s):  
Residual Life ◽  
Mean Residual Life ◽  
Lifetime Distributions

scholarly journals Mathematical Modeling of Survivability Function for Thermoelectric Module

2021 ◽  
Vol 2056 (1) ◽  
pp. 012028
Author(s):  
Sh Sattar ◽  
A Osipkov ◽  
V V Belyaev
Keyword(s):  
Mechanical Stress ◽  
Residual Life ◽  
Survival Function ◽  
Thermoelectric Module ◽  
High Ratio ◽  
Distribution Model ◽  
Mean Residual Life ◽  
Reliability Model ◽  
Strength Based ◽  
Selection Of

Abstract Developing an optimized reliability model for thermoelectric module at the stress where the probability of module to functions without abruptive failure is a challenging aspect. One of the major reasons is the mismatch of thermal expansion coefficient, which has severe effects on segmented moduli compared to unsegmented moduli. The likelihood of a thermoelectric module to survive at certain level of thermo-mechanical stresses varies by varying number of component (layers) in thermoelectric leg. On another hand, selection of an adequate distribution model to predict reliability and sustainability of the thermoelectric module requires development of new optimized stress-strength-based model. In this paper the predictive reliability model for high temperature segmented module is derived from parametric Lognormal mean residual life and nonparametric Lognormal-kernel survival function to measure probability of module to survive at certain thermo-mechanical stress. A comprehensive comparative discussion has been done to illustrate the maximum likelihood based on Bayesian nonparametric lognormal-Kernel inference method regarding to Monte Carlo simulation, Weibull’s distribution, and Lognormal mean residual life for various shapes for the survival function. It has been demonstrated that nonparametric lognormal-kernel survival function has high ratio of probability to predict the survival of module at higher discrete thermo-mechanical stress data.

MEAN RESIDUAL LIFE FUNCTION FOR ADDITIVE AND MULTIPLICATIVE HAZARD RATE MODELS

2015 ◽  
Vol 30 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Ramesh C. Gupta
Keyword(s):  
Hazard Rate ◽  
Residual Life ◽  
Turning Points ◽  
Sufficient Conditions ◽  
Mean Residual Life ◽  
Residual Life Function ◽  
Mean Residual Life Function ◽  
Hazard Rate Models ◽  
The Mean ◽  
Life Function

This paper deals with the mean residual life function (MRLF) and its monotonicity in the case of additive and multiplicative hazard rate models. It is shown that additive (multiplicative) hazard rate does not imply reduced (proportional) MRLF and vice versa. Necessary and sufficient conditions are obtained for the two models to hold simultaneously. In the case of non-monotonic failure rates, the location of the turning points of the MRLF is investigated in both the cases. The case of random additive and multiplicative hazard rate is also studied. The monotonicity of the mean residual life is studied along with the location of the turning points. Examples are provided to illustrate the results.

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