On computation of NPMLE for middle-censored data

Mangalam, Vasudevan; Nair, Gopalan; Zhao, Yun

doi:10.1016/j.spl.2007.12.023

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…The proposed scheme can be considered as a special case of the middle censoring model which was introduced by Jammalamadaka and Mangalam [9] for which all random intervals are the same. The later plan is also studied by Jammalamadaka and Iyer [10] and Mangalam et al [11] in the non-parametric set-up and Iyer et al [2] in the parametric set-up.…”

Section: Introductionmentioning

confidence: 95%

Bayesian reconstruction of the missing failure times in exponential distribution

Khatib

Ahmadi

Razmkhah

2013

Journal of Statistical Computation and Simulation

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In this paper, we consider the situations in which a middle part of the sample is lost or removed from the experiment. The left-censored sample and Type-II right-censored sample are special cases of this scheme. Point and interval reconstructors for lost failure times are obtained, while the underlying distribution is exponential. To illustrate the performance of the proposed procedures, two numerical examples based on the real data sets are presented. In the case of two-parameter, a simulation study has been done and also the sensitivity of the results to deviations from the true distribution is investigated.

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Section: Introductionmentioning

confidence: 95%

Bayesian reconstruction of the missing failure times in exponential distribution

Khatib

Ahmadi

Razmkhah

2013

Journal of Statistical Computation and Simulation

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“…They proved that the NPMLE is always a self-consistent estimator (SCE) (Tarpey & Flury, 1996). Some rigorous treatments of this censoring scheme are found in Jammalamadaka & Iyer (2004), Iyer et al (2008), Mangalam et al (2008), Jammalamadaka & Mangalam (2009), Shen (2010Shen ( , 2011, Davarzani & Parsian (2011) and Davarzani et al (2015).…”

Section: Introductionmentioning

confidence: 99%

An Additive Risks Regression Model for Middle-Censored Lifetime Data

Sankaran

Prasad

2017

Statistics in Transition New Series

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Middle-censoring refers to data arising in situations where the exact lifetime of study subjects becomes unobservable if it happens to fall in a random censoring interval. In the present paper we propose a semiparametric additive risks regression model for analysing middle-censored lifetime data arising from an unknown population. We estimate the regression parameters and the unknown baseline survival function by two different methods. The first method uses the martingale-based theory and the second method is an iterative method. We report simulation studies to assess the finite sample behaviour of the estimators. Then, we illustrate the utility of the model with a real life data set. The paper ends with a conclusion.

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“…They established the asymptotic properties ofF 0 S and provided an upper bound of the difference between F 0 and F 0 0 . Mangalam et al (2008) showed that under condition (A) every censoring interval contains at least one uncensored observation, i.e. d i ¼ 0 implies that there exists j such that d j ¼ 1 and X j 2 ðU i ,V i Þ, the solution of Eq.…”

Section: Introductionmentioning

confidence: 99%

“…(1.3) will be unique, and as a consequenceF n will be equal to an NPMLE. Mangalam et al (2008) proposed a technique for obtaining the NPMLE by dividing the original problem into subproblems.…”

Section: Introductionmentioning

confidence: 99%