On E-Bayesian estimations for the cumulative hazard rate and mean residual life under generalized inverted exponential distribution and type-II censoring

2019 ◽  
Vol 47 (5) ◽  
pp. 865-889 ◽  
Author(s):  
Hassan Piriaei ◽  
Gholamhossein Yari ◽  
Rahman Farnoosh
Keyword(s):  
Exponential Distribution ◽  
Hazard Rate ◽  
Residual Life ◽  
Mean Residual Life ◽  
Type Ii ◽  
Cumulative Hazard ◽  
Type Ii Censoring ◽  
Bayesian Estimations

scholarly journals Sharp bounds for exponential approximations under a hazard rate upper bound

2015 ◽  
Vol 52 (03) ◽  
pp. 841-850 ◽  
Author(s):  
Mark Brown
Keyword(s):  
Exponential Distribution ◽  
Hazard Rate ◽  
Residual Life ◽  
Continuous Distribution ◽  
Rate Function ◽  
Mean Residual Life ◽  
Birth And Death Processes ◽  
Absolutely Continuous ◽  
Sharp Bounds ◽  
Life Distributions

Consider an absolutely continuous distribution on [0, ∞) with finite meanμand hazard rate functionh(t) ≤bfor allt. Forbμclose to 1, we would expectFto be approximately exponential. In this paper we obtain sharp bounds for the Kolmogorov distance betweenFand an exponential distribution with meanμ, as well as betweenFand an exponential distribution with failure rateb. We apply these bounds to several examples. Applications are presented to geometric convolutions, birth and death processes, first-passage times, and to decreasing mean residual life distributions.

scholarly journals Sharp bounds for exponential approximations under a hazard rate upper bound

2015 ◽  
Vol 52 (3) ◽  
pp. 841-850 ◽  
Author(s):  
Mark Brown
Keyword(s):  
Exponential Distribution ◽  
Hazard Rate ◽  
Residual Life ◽  
Continuous Distribution ◽  
Rate Function ◽  
Mean Residual Life ◽  
Birth And Death Processes ◽  
Absolutely Continuous ◽  
Sharp Bounds ◽  
Life Distributions

Consider an absolutely continuous distribution on [0, ∞) with finite mean μ and hazard rate function h(t) ≤ b for all t. For bμ close to 1, we would expect F to be approximately exponential. In this paper we obtain sharp bounds for the Kolmogorov distance between F and an exponential distribution with mean μ, as well as between F and an exponential distribution with failure rate b. We apply these bounds to several examples. Applications are presented to geometric convolutions, birth and death processes, first-passage times, and to decreasing mean residual life distributions.

On estimation ofR=P(Y

2012 ◽  
Vol 82 (5) ◽  
pp. 729-744 ◽  
Author(s):  
Buğra Saraçoğlu ◽  
Ismail Kinaci ◽  
Debasis Kundu
Keyword(s):  
Exponential Distribution ◽  
Type Ii ◽  
Type Ii Censoring ◽  
Progressive Type ◽  
Progressive Type Ii Censoring
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