• Journal of Korea Water Resources Association
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• v.42 no.4
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• pp.331-341
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• 2009

In this study, we compared the observed information matrix with the Fisher information matrix to estimate the uncertainty of maximum likelihood estimators of the generalized logistic (GL) distribution. The previous literatures recommended the use of the observed information matrix because this is convenient since this matrix is determined as the part of the parameter estimation procedure and there is little difference in accuracy between the observed information matrix and the Fisher information matrix for large sample size. The observed information matrix has been applied for the generalized logistic distribution based on the previous study without verification. For this purpose, a simulation experiment was performed to verify which matrix gave the better accuracy for the GL model. The simulation results showed that the variance-covariance of the ML parameters for the GL distribution came up with similar results to those of previous literature, but it is preferable to use of the Fisher information matrix to estimate the uncertainty of quantile of ML estimators.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.415-433
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• 1999

This paper considers the design of step-stress accelerated life tests for the Weibull distribution with a nonconstant shape parameter under Type I censoring. It is assumed that scale and shape parameters are log-linear functions of (possibly transformed) stress and that a cumulative exposure model holds for the effect of changing stress. The asymptotic variance of the maximum likelihood estimator of a stated quantile at design stress is used as an optimality criterion. The optimum three step-stress plans are presented for selected values of design parameters and the effects of errors in pre- estimates of the design parameters are investigated.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.208-219
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• 2004

This paper considers a nonparametric estimation of lifetime distribution for ramp stress tests with stress bound under intermittent inspection. The test items are inspected only at specified time points an⊂1 so the collected observations are grouped data. Under the cumulative exposure model, two nonparametric estimation methods of estimating the lifetime distribution at use condition stress are proposed for the situation which the time transformation function relating stress to lifetime is a type of the inverse power law. Each of items is initially put on test under ramp stress and then survivors are put on test under constant stress, where all failures in the Inspection interval are assumed to occur at the midi)oint or the endpoint of that interval. Two proposed estimators of quantile from grouped data consisting of the number of items failed in each inspection interval are numerically compared with the maximum likelihood estimator(MLE) based on Weibull distribution.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.411-420
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• 2005

In this study, the MIVQUE estimators of variance components for the one-way random model with unbalanced data are investigated. In order to compare the efficiency of MIVQUE estimators obtained by using three priori estimates, the Empirical Quantile Dispersion Graphs (EQDGs) are used. From the results of Monte-Carlo study, the MIVQUE estimator using ${\sigma}^2_{\alpha}\;=\;0\;and\;{\sigma}^2_{varraho}=1$ as the priori estimate performs well relative to other estimators.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.859-871
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• 2003

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the slope of regression model under the symmetric and continuous underlying error distributions. This selector statistics is based on the residuals after the preliminary fit L$_1$ (least absolute estimator) and the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying symmetric distributions in the location model. If we use L$_1$ as a preliminary fit to get residuals, we find the asymptotic distribution of sample quantiles of residual are slightly different from that of sample quantiles in the location model. If we use the functions of sample quantiles of residuals as selector statistics, we find the suitable quantile points of residual based on maximizing the asymptotic distance index to discriminate distributions under consideration. In Monte Carlo study, this adaptive M-estimation method using selector statistics works pretty good in wide range of underlying error distributions.

• Journal of Korean Society for Quality Management
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• v.24 no.3
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• pp.37-49
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• 1996

For highly reliable devices it is often defined to "fail" when its performance degrades below a specified value. In this paper we consider a method for optimally designing accelerated degradation tests(ADTs) in which the performance over exposure time and stress has Weibull distribution. For the product whose performance has Weibull distribution, the optimum plan - low stress level and sample proportions allocated to each test condition - is obtained, which minimize the asymptotic variance of maximum likelihood estimator of a stated quantile at design stress. We also present compromise ADTs plan that can be used for the practical purpose.

• Journal of Korean Society for Quality Management
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• v.40 no.2
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• pp.117-125
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• 2012

For the highly reliable products, an accelerated degradation test (ADT) is a useful tool which has been employed in industry to obtain reliability-related information within an affordable amount of time and cost. In an ADT, as all other reliability tests, it is important to carefully design the ADT beforehand to obtain estimates of the quantities of interest as precisely as possible. In this paper, optimal ADTs are developed assuming that the constant-stress loading method is employed and the degradation characteristic follows a Wiener process. Under the constraint that the total cost does not exceed a pre-specified budget, the stress levels, the number of test units allocated to each stress level and the number of measurement (termination time) are determined such that the asymptotic variance of the maximum likelihood estimator of the q-th quantile of the lifetime distribution at the use condition is minimized.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.53-73
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• 1996

This paper compares two accelerated life for Weibull distribution. One is the optimum constant stress accelerated life test which minimizes the asymptotic variance of maximum likelihood estimator of a specified quantile at design stress, and the other is corresponding simple step stress test. The models and optimum designs of constant stress and step stress tests are reviewed. Behaviors of asymptotic variances, effects of design parameters to optimum tests, and expected numbers of failures and expected test times of the two tests are investigated. The efficiency of step stress test relative to constant stress test is studied in terms of variance ratio, and robustness to preestimates of design parameters are investigated.

• International Journal of Reliability and Applications
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• v.15 no.1
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• pp.23-50
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• 2014

This paper deals with formulation of optimum multi-objective modified step-stress accelerated life test (ALT) plan for Burr type-XII distribution under type-I censoring. Since it is impractical to estimate only one objective parameter after conducting costly ALT tests; also, it is not desirable to assume instantaneous changes in stress levels because of limited capacity of test equipments and the presence of undesirable failure modes, therefore, an optimum multi-objective modified step-stress ALT plan has been designed. The optimal test plan consists in determining the optimum low stress level and optimal time at which stress starts linearly increasing from low stress by minimizing the weighted sum of the asymptotic variances of the maximum likelihood estimator of quantile lifetimes at design constant stress. The method developed has been illustrated using an example. Sensitivity analysis has been carried out. Comparative study has also been done to highlight the merits of the proposed model.

• Journal of Applied Reliability
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• v.13 no.2
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• pp.87-98
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• 2013

Accelerated degradation tests (ADTs) measuring failure-related degradation characteristic at the accelerated condition are widely used to assess the reliability of highly reliable products. Often, however, little degradation could be observed even in single-stress ADTs due to the high reliability of test unit, and as a result poor estimate of the reliability may be obtained. ADTs with multiple stress variables can be employed to overcome such difficulties. In this paper, optimal ADT plans with two stress variables are developed assuming that the degradation characteristic follows Weibull distribution by determining the stress levels, the proportion of test units allocated to each stress level such that the asymptotic variance of the maximum likelihood estimator of the q-th quantile of the lifetime distribution at the use condition is minimized.