• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.219-225
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• 2012

We derive approximate maximum likelihood estimators of two parameters in a weighted exponential distribution, and derive the density function for the ratio Y=(X+Y) of two independent weighted exponential random variables X and Y, and then observe the skewness of the ratio density.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1335-1344
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• 2008

The maximum likelihood method does not admit explicit solutions when the sample is multiply censored and progressive censored. So we shall propose some approximate maximum likelihood estimators (AMLEs) of the scale parameter for the power function distribution based on multiply Type-II censored samples and progressive Type-II censored samples when shape parameter is known. We compare the proposed estimators in the sense of the mean squared error (MSE) through Monte Carlo simulation for various censoring schemes.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.793-801
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• 2007

For multiply Type-II censored samples from a half-triangle distribution, the maximum likelihood method does not admit explicit solutions. In this case, we propose some explicit estimators of the location parameter in the half-triangle distribution by the approximate maximum likelihood methods. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.817-826
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• 2007

In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the location parameter in a generalized extreme value distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.47-64
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• 2020

In this paper, we derive some estimators of the scale parameter of the exponentiated half-logistic distribution based on the multiply Type-I hybrid censoring scheme. We assume that the shape parameter λ is known. We obtain the maximum likelihood estimator of the scale parameter σ. The scale parameter is estimated by approximating the given likelihood function using two different Taylor series expansions since the likelihood equation is not explicitly solved. We also obtain Bayes estimators using prior distribution. To obtain the Bayes estimators, we use the squared error loss function and general entropy loss function (shape parameter q = -0.5, 1.0). We also derive interval estimation such as the asymptotic confidence interval, the credible interval, and the highest posterior density interval. Finally, we compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. The average length of 95% intervals and the corresponding coverage probability are also obtained.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.8
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• pp.1035-1039
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• 2000

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.31-52
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• 2008

In this paper generalized version of the geometric distribution is introduced. This distribution can be considered as a two-parameter generalization of the discrete geometric distribution. The main statistical and reliability properties of this distribution are discussed. Two methods of estimation, namely maximum likelihood method and the method of moments are used to estimate the parameters of this distribution. Simulation is utilized to calculate these estimates and to study some of their properties. Also, asymptotic confidence limits are established for the maximum likelihood estimates. Finally, the appropriateness of this new distribution for a set of real data, compared with the geometric distribution, is shown by using the likelihood ratio test and the Kolmogorove-Smirnove test.

• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.33-46
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• 2003

The continual reassessment method (CRM) provides a Bayesian estimation of the maximum tolerated dose (MTD) in phase I clinical trials. The CRM has been proposed as an alternative design of the standard design. The CRM has been modified to improve practical feasibility and, recently, the likelihood approach CRM has been proposed. In this paper we investigate the consistency and asymptotic normality of the modified likelihood approach CRM in which the maximum likelihood estimate is used instead of the posterior mean. Small-sample properties of the consistency is examined using complete enumeration. Both the asymptotic results and their small-sample properties show that the modified CRML outperforms the standard design.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.85-88
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• 2001

In this paper we consider a problem of blind co-channel signal separation, the goal of which is to estimate multiple co-channel digitally modulated signals using an antenna array. We employ the joint maximum likelihood estimation and present a sequential algorithm, which is referred to as sequential joint maximum likelihood (SJML) algorithm. It separates multiple co-channel signal on-line and converges fast in overdetermined noisy communication environment. And the computational complexity of SJML for M-QAM (M=8, 16, 64,...) signals is less expensive compared to the SLSP. Useful behavior of this algorithm are confirmed by simulations.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.815-823
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• 2008

In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a half-logistic distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimators of the reliability function using the proposed estimators. We compare the proposed estimators in the sense of the mean squared error.