Estimation of parameters from progressively censored data using EM algorithm

Ng, Hon Keung Tony; Chan, Ping Shing; Balakrishnan, N.

doi:10.1016/s0167-9473(01)00091-3

Cited by 213 publications

(103 citation statements)

References 8 publications

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“…The main drawback of the EM algorithm is its rather slow convergence, compared to the Newton-Raphson method, when the "missing data" contain relatively large amount of information (Little and Rubin, 1983). Recently, several researchers have used the EM method such as Adamidis et al (2005), Karlis (2003), Ng et al (2002), Adamidis and Loukas (1998), Adamidis (1999), among others. Newton-Raphson is required for the M-step of the EM algorithm.…”

Section: Estimationmentioning

confidence: 99%

The Exponential-Generalized Truncated Geometric (EGTG) Distribution: A New Lifetime Distribution

Rahmouni

Orabi

2017

IJSP

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This paper introduces a new two-parameter lifetime distribution, called the exponential-generalized truncated geometric (EGTG) distribution, by compounding the exponential with the generalized truncated geometric distributions. The new distribution involves two important known distributions, i.e., the exponential-geometric (Adamidis and Loukas, 1998) and the extended (complementary) exponential-geometric distributions (Adamidis et al., 2005; in the minimum and maximum lifetime cases, respectively. General forms of the probability distribution, the survival and the failure rate functions as well as their properties are presented for some special cases. The application study is illustrated based on two real data sets.

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Section: Estimationmentioning

confidence: 99%

The Exponential-Generalized Truncated Geometric (EGTG) Distribution: A New Lifetime Distribution

Rahmouni

Orabi

2017

IJSP

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“…As pointed out by Little and Rubin (1983), the EM algorithm converge reliably but rather slowly, compared withthe Newton-Raphson method, when the amount of information in the missing data is relatively large. Recently, EM algorithm has been used by such researchers asAdamidis and Loukas (1998), Adamidis (1999), Ng et al (2002), Karlis (2003), and Adamidis et al (2005).…”

Section: Em Algorithmmentioning

confidence: 99%

Generalizedextended Weibull Power Series Family of Distributions

Alkarni¹

2015

ARMS

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In this study, we introduce a new familyof models for lifetime data called generalized extended Weibullpower series family of distributions by compoundinggeneralizedextended Weibull distributions and power series distributions. The compounding procedure follows the same setup carried out by Adamidis (1998). The proposed family contains all types of combinations between truncated discrete with generalized and nongeneralized Weibull distributions. Some existing power series and subclasses of mixed lifetime distributions become special cases of the proposed family, such as the compound class of extended Weibull power seriesdistributions proposed by Silva et al. (2013) and the generalized exponential power series distributionsintroduced by Mahmoudi and Jafari (2012).Some mathematical properties of the new class are studied, includingthe cumulative distribution function, density function, survival function, and hazard rate function. The method of maximum likelihood is used for obtaining a general setup for estimating the parameters of any distribution in this class. An expectation-maximization algorithm is introduced for estimating maximum likelihood estimates.Special subclasses and applications for some models in areal datasetare introduced to demonstrate the flexibility and the benefit of this new family.

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“…Since the distribution of Weibull became widely recognized, various methods have been proposed to estimate its parameters [2,17,19].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for Weibull distribution with right censored data using EM algorithm

Ferreira¹,

Silva²

2017

EiN

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Estimation of parameters from progressively censored data using EM algorithm

Cited by 213 publications

References 8 publications

The Exponential-Generalized Truncated Geometric (EGTG) Distribution: A New Lifetime Distribution

The Exponential-Generalized Truncated Geometric (EGTG) Distribution: A New Lifetime Distribution

Generalizedextended Weibull Power Series Family of Distributions

Parameter estimation for Weibull distribution with right censored data using EM algorithm

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