• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.241-250
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• 2016

In this paper, we consider statistical inferences on the estimation of the parameters of a Weibull distribution when data are randomly censored. Maximum likelihood estimators (MLEs) and approximate MLEs are derived to estimate the parameters. We consider two cases for the censoring model: the assumption that the censoring distribution does not involve any parameters of interest and a censoring distribution that follows a Weibull distribution. A simulation study is conducted to compare the performances of the estimators. The result shows that the MLEs and the approximate MLEs are similar in terms of biases and mean square errors; in addition, the assumption of the censoring model has a strong influence on the estimation of scale parameter.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.647-658
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• 2018

In this paper, we study statistical inferences on the maximum likelihood estimation of a normal distribution when data are randomly censored. Likelihood equations are derived assuming that the censoring distribution does not involve any parameters of interest. The maximum likelihood estimators (MLEs) of the censored normal distribution do not have an explicit form, and it should be solved in an iterative way. We consider a simple method to derive an explicit form of the approximate MLEs with no iterations by expanding the nonlinear parts of the likelihood equations in Taylor series around some suitable points. The points are closely related to Kaplan-Meier estimators. By using the same method, the observed Fisher information is also approximated to obtain asymptotic variances of the estimators. An illustrative example is presented, and a simulation study is conducted to compare the performances of the estimators. In addition to their explicit form, the approximate MLEs are as efficient as the MLEs in terms of variances.

• Journal of Life Science
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• v.9 no.2
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• pp.176-181
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• 1999

In order to understand molecular phylogenetics of silkworm (Bombyx mori), we analyzed the sequences of BmoMAR isolated from Bomhyx mori that is partial coding gene of transposase of mariner-like element(MLE). By pairwise comparing nucleotide sequences of BmoMAR with ten previously reported insect MLEs accessed in GeneBank, the average genetic distance was estimated to be 0.4840. The phylogenetics tree constructed from nine insect species except for human MLE(Hsmarl) by UPGMA method indicated that MLEs are divided into three clusters, and Drosophila mariutiana was independently subgrouped. Bombyx mori(BmoMAR) was subgrouped with microcaddishfly (Orthotrichia cristata), webworm(Atteva punctella), almond moth(Ephestia cautella), Hyalopora cecropia which we lepidoptera. Phylogenetics tree according to UPGMA principle, on the basis of informative nucleotide sequences of nine insect MLEs, indicated that Bombyx mori was more closely related to microcaddishfly(Orthotrichia cristata) and webworm (Atteva punctella) of lepidoptera. We suggest that insect MLEs are a useful key for studying molecular phylogenetics among intra species of insects.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.25-55
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• 1998

Maximum likelihood estimates (MLEs) for recursive models of categorical variables are discussed under an EM framework. Since MLEs by EM often depend on the choice of the initial values for MLEs, we explore reasonable rules for selecting the initial values for EM. Simulation results strongly support the proposed rules.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.87-94
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• 1994

In this paper, we consider optimum plan to determine stress change times under the three-step stress PALTs, assuming that each test units follows an exponential distribution. The tampered random variable(TRV) model for the three-step stress PALTs setup are introduced, and maximum likelihood estimators(MLEs) of the failure rate and the acceleration factors are obtained. The change times to minimize the generalized asymptotic variance(GAVR) of MLEs of the failure rate and the acceleration factors are proposed for the three-step stress PALTs.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.29-39
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• 2013

This paper derives maximum likelihood estimators (MLEs) and some approximate MLEs (AMLEs) of unknown parameters of the generalized exponential distribution when data are lower record values. We derive approximate Bayes estimators through importance sampling and obtain corresponding Bayes predictive intervals for unknown parameters for lower record values from the generalized exponential distribution. For illustrative purposes, we examine the validity of the proposed estimation method by using real and simulated data.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.185-192
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• 1998

A bivariate extension of the two-parameter exponential distribution is proposed as a model for certain problems in system level life testing. In particular, it applies to two-component shared parallel systems having a minimum guarantee time. Various statistical properties of the model are investigated, including maximum likelihood estimators (MLEs), modified MLEs, and unbiased estimators of the parameters and their distributions.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.269-285
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• 2016

In this paper, a simple step-stress accelerated life test (ALT) under progressive type-II censoring is considered. Progressive type-II censoring and accelerated life testing are provided to decrease the lifetime of testing and lower test expenses. The cumulative exposure model is assumed when the lifetime of test units follows an extension of the exponential distribution. Maximum likelihood estimates (MLEs) and Bayes estimates (BEs) of the model parameters are also obtained. In addition, a real dataset is analyzed to illustrate the proposed procedures. Approximate, bootstrap and credible confidence intervals (CIs) of the estimators are then derived. Finally, the accuracy of the MLEs and BEs for the model parameters is investigated through simulation studies.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1249-1257
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• 2012

In this paper, we derive maximum likelihood estimators (MLEs) and approximate MLEs (AMLEs) of the unknown parameters in a generalized half logistic distribution when the data are upper record values. As an illustration, we examine the validity of our estimation using real data and simulated data. Finally, we compare the proposed estimators in the sense of the mean squared error (MSE) through a Monte Carlo simulation for various record values of size.