2011
DOI: 10.5402/2011/203618
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Estimating and Planning Accelerated Life Test Using Constant Stress for Generalized Logistic Distribution under Type-I Censoring

Abstract: The optimal designs and statistical inference of accelerated life tests under type-I are studied for constant stress-accelerated life tests (CSALTs). It is assumed that the lifetime at design stress has generalized logistic distribution. The scale parameter of the lifetime distribution at constant stress levels is assumed to be an inverse power law function of the stress level. The maximum likelihood (ML) estimators of the model parameters, Fisher information matrix, the asymptomatic variance-covariance matrix… Show more

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Cited by 8 publications
(6 citation statements)
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“…In this section, we will present a numerical investigation of the maximum likelihood estimation for the parameters of Exponentiated Pareto distribution (,  ) . Where  is is the shape parameter which is affected by the stress by using the power rule model defined by equation (4). We estimate the three parameters  and , p c .…”
Section: -Numerical Resultsmentioning
confidence: 99%
“…In this section, we will present a numerical investigation of the maximum likelihood estimation for the parameters of Exponentiated Pareto distribution (,  ) . Where  is is the shape parameter which is affected by the stress by using the power rule model defined by equation (4). We estimate the three parameters  and , p c .…”
Section: -Numerical Resultsmentioning
confidence: 99%
“…The progressively type-I censoring scheme is used in multiple lifetime models, like Refs. [ 13 , 14 , 15 ]. Many scholars have studied progressively type-II censoring scheme, too.…”
Section: Introductionmentioning
confidence: 99%
“…[10] created a general design for constant stress ALT with multi experimental factors. [5] considered the optimal designs and statistical inference of ALT based on time constant stress for generalized logistic distribution. [4] constructed the optimal of constant stress ALT using Kumaraswamy Weibull distribution and a log-linear relationship between the stress and the shape parameter applying maximum likelihood (ML) estimation approach.…”
Section: Introductionmentioning
confidence: 99%