• Journal of the Korean Statistical Society
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• v.13 no.1
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• pp.1-19
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• 1984

A random effects model of a one-way layout contingency table is developed using a Dirichlet-multinomial distribution. A test statistic, say $T_k$, is suggested for detecting Dirichlet-multinomial departure from a multinomial distribution. It is shown that the $T_k$ test is asymptotically superior to the classical chi-square test based on the asymptotic relative efficiency. This superiority is further evidenced by a Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.423-432
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• 1999

Accelerated life testing of product is commonly used to reduced test time and costs. In this papers is considered when the product is a two component system with lifetimes following the bivariate exponential distribution of Sarkar(1987) using inverse power rule model. Also we derived the maximum likelihood estimators of parameters for asymptotic normality. We compare the mean square error of the proposed estimator for the life distribution under use conditions stree through Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.291-297
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• 2001

For a stochastic regression model in which the errors are assumed to form a stationary linear process, we show that the difference between the empirical distribution functions of the errors and the estimates of those errors converges uniformly in probability to zero at the rate of $o_{p}$ ( $n^{-}$$\frac{1}{2}$) as the sample size n increases.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.509-518
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• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.1-19
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• 2021

Heavy tailed distributions are useful for modeling actuarial and financial risk management problems. Actuaries often search for finding distributions that provide the best fit to heavy tailed data sets. In the present work, we introduce a new class of heavy tailed distributions of a special sub-model of the proposed family, called a new extended alpha power transformed Weibull distribution, useful for modeling heavy tailed data sets. Mathematical properties along with certain characterizations of the proposed distribution are presented. Maximum likelihood estimates of the model parameters are obtained. A simulation study is provided to evaluate the performance of the maximum likelihood estimators. Actuarial measures such as Value at Risk and Tail Value at Risk are also calculated. Further, a simulation study based on the actuarial measures is done. Finally, an application of the proposed model to a heavy tailed data set is presented. The proposed distribution is compared with some well-known (i) two-parameter models, (ii) three-parameter models and (iii) four-parameter models.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.471-488
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• 2018

In this paper, we propose extending proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured. Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. This proposal also allows for a realistic description of non-risk individuals, since individuals cured due to intrinsic factors (immunes) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. We put the proposed model in a Bayesian framework and use a Markov chain Monte Carlo algorithm for the computation of posterior distribution. A simulation study is conducted to assess the proposed model and the computation algorithm. We also discuss model selection based on pseudo-Bayes factors as well as developing case influence diagnostics for the joint posterior distribution through ${\psi}-divergence$ measures. The motivating cutaneous melanoma data is analyzed for illustration purposes.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2110-2117
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• 2003

A physics-based material removal model in various scales is described and a feature scale simulation for a chemical mechanical polishing (CMP) process is performed in this work. Three different scales are considered in this model, i.e., abrasive particle scale, asperity scale and wafer scale. The abrasive particle and the asperity scales are combined together and then homogenized to result in force balance conditions to be satisfied in the wafer scale using an extended Greenwood-Williamson and Whitehouse-Archard statistical model that takes into consideration the joint distribution of asperity heights and asperity tip radii. The final computation is made to evaluate the material removal rate in wafer scale and a computer simulation is performed for detailed surface profile variations on a representative feature. The results show the dependence of the material removal rate on the joint distribution, applied external pressure, relative velocity, and other operating conditions and design parameters.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.369-378
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• 2006

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.2
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• pp.15-22
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• 1980

For efficient coding of graphic image data, the statistical characteristics for both Korean lettered images and English lettered images are measurpd and co mpared. Also, the measured run length distribution is compared with the run length distribution hased on Markov model. It is shown that the measured white run length distribution is more Bike a negative - power distribution than an exponential distribution . This fact is stronger in the Korean lettered images than is the English lettered images, The performances of four typical run length codes are compared for the same set of graphic data files,, and it is shown that the codes perform better in the Korean ]entered images :hart In Eng]isle lettered images.