Some Reliability Properties of the Inactivity Time

Kundu, Chanchal; Nanda, Asok K.

doi:10.1080/03610920902807895

Cited by 47 publications

(25 citation statements)

References 19 publications

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“…Based on EIT, Chandra 3 and Roy (2001) presented some characterizations of distributions. Recently, Kundu and Nanda (2010) also considered m(t) 4 for characterizations of quite a few distributions. Goliforushani and Asadi (2008) look up the concept of the expected 5 inactivity time and applied it to a discrete random variable.…”

Section: F Goodarzi Et Al / Statistics and Probability Letters XX (mentioning

confidence: 98%

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On upper bounds for the variance of functions of the inactivity time

Goodarzi

Amini

Borzadaran

2016

Statistics & Probability Letters

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Section: F Goodarzi Et Al / Statistics and Probability Letters XX (mentioning

confidence: 98%

“…Kundu and Nanda (2010) have shown that if the components of a system have a 31 common DRHR distribution then M r n (t) is an increasing function of t. They also showed that M r n (t) for fixed n is a decreasing 32 function of r, r = 1, 2, . .…”

mentioning

confidence: 94%

On upper bounds for the variance of functions of the inactivity time

Goodarzi

Amini

Borzadaran

2016

Statistics & Probability Letters

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“…The reversed residual life can be defined as the conditional random variable − | ≤ which denotes the time elapsed from the failure of a component given that its life is less than or equal to t. This random variable may also be called the inactivity time (or time since failure); for more details, you may (see, Kundu and Nanda, (2010) and Nanda et al (2003). Also, in reliability, the mean reversed residual life and ratio of two consecutive moments of reversed residual life characterize the distribution uniquely.…”

Section: Published By Atlantis Pressmentioning

confidence: 99%

“…Proof: Let X be a random variable with density function (11). The r th ordinary moment of the distribution is given by…”

Section: Theoremmentioning

confidence: 99%

The Additive Weibull-Geometric Distribution: Theory and Applications

Elbatal¹,

Mansour²,

Ahsanullah³

2016

JSTA

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In this paper, we introduce a new class of lifetime distributions which is called the additive Weibull geometric (AWG) distribution. This distribution obtained by compounding the additive Weibull and geometric distributions.The new distribution has a number of well-known lifetime special sub-models such as modified Weibull geometric, Weibull geometric, exponential geometric, among several others. Some structural properties of the proposed new distribution are discussed. We propose the method of maximum likelihood for estimating the model parameters and obtain the observed information matrix. A real data set is used to illustrate the importance and flexibility of the new distribution.

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“…The reversed residual life can be defined as the conditional random variable t − X|X ≤ t which denotes the time elapsed from the failure of a component given that its life is less than or equal to t. This random variable may also be called the inactivity time (or time since failure); for more details see Kundu and Nanda (2010) and Nanda et al (2003). Also, in reliability, the mean reversed residual life (MRRL) and ratio of two consecutive moments of reversed residual life characterize the distribution uniquely.…”

Section: The τ Th Order Moment Of the Residual Life And Reversed Resimentioning

confidence: 99%