• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.127-139
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• 1995

This paper is concerned with the asymptotic properties of the least absolute deviation estimators for nonlinear regression models. The simple and practical sufficient conditions for the strong consistency and the asymptotic normality of the least absolute deviation estimators are given. It is confirmed that the extension of these properties to wide class of regression functions can be established by imposing some condition on the input values. A confidence region based on the least absolute deviation estimators is proposed and some desirable asymptotic properties including the asymptotic relative efficiency also discussed for various error distributions. Some examples are given to illustrate the application of main results.

• Journal of the Korean Statistical Society
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• v.34 no.3
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• pp.219-233
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• 2005

The 3-way balanced multi-level rotation design has been discussed (Park Kim and Kim, 2003), where the 3-way balancing is done on interview time, in monthly sample and rotation group and recall time. A greater advantage of 3-way balanced design is accomplished by an estimator. To obtain the advantage, we generalized previous generalized composite estimator (GCE). We call this as l-step GCE. The variance of the l-step GCE's of various characteristics of interest are presented. Also, we provide the coefficients which minimize the variance of the l-step GCE. Minimizing a weighted sum of variances of all concerned estimators of interest, we drive one set of the compromise coefficient of l-step GCE's to preserve additivity of estimates.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.563-573
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• 2000

In this thesis, we propose the test statistic for ordered alternatives on c-sample ranked set samples(RSS). The proposed test statistic JRSS is Jonckheere type statistic using the median of the i-th samples in each cycle. We obtained the asymptotic property of the proposed test statistic and the asymptotic relative efficiencies of the proposed test statistic with respect to J SRS which Jonckheere type statistic on simple random samples(SRS). From the simulation works, J RSS is superior to J SRS. We compared the empirical powers of J RSS with respect to U RSS on ranked set sample and U SRS on simple random sample using all samples, which are proposed by Kim, Kim and Lee(1999). The powers of J RSS are nearly the same values when entire sample size is large. J RSS is superior to U RSS. J RSS is simpler than U RSSon calculating process.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.491-501
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• 2006

We consider a multiple change point model with autoregressive conditional heteroscedasticity (ARCH). The model assumes that all or the part of the parameters in the ARCH equation change over time. The occurrence of the change points is modelled as the discrete time Markov process with unknown transition probabilities. The model is estimated by Markov chain Monte Carlo methods based on the approach of Chib (1998). Simulation is performed using a variant of perfect sampling algorithm to achieve the accuracy and efficiency. We apply the proposed model to the simulated data for verifying the usefulness of the model.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.395-406
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• 2001

In this paper, we propose the test statistic for the umbrella alternatives on c-samples ranked set samples(RSS), where the peak of the umbrella is known. We obtain the asymptotic property of the proposed test statistic and the asymptotic relative efficiencies of the proposed test statistic with respect to U-statistic based on simple random samples(SRS). From the simulation work, we compare the empirical powers of the proposed test statistic with U-statistic based on SRS.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.255-264
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• 2015

This paper addressed the problem of estimation of finite population mean in double sampling for stratification. This paper proposed a generalized ratio-cum-product type estimator of population mean. The bias and mean square error of the proposed estimator has been obtained upto the first degree of approximation. A particular member of the proposed generalized estimator was identified and studied from a comparison point of view. It is observed that the identified particular estimator is more efficient than usual unbiased estimator and Ige and Tripathi (1987) estimators. An empirical study was conducted in support of the theoretical findings.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• The Korean Journal of Applied Statistics
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• v.2 no.1
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• pp.18-34
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• 1989

The problem of simultaneously estimating the pairwise differences of means of four independent normal populations with equal variances is considered. A statistical computing procedure involving a trivariate t density constructs the exact confidence intervals with simultaneous co verage probability equal to $1-\alpha$. For equal sample sizes, the new procedure is the same as the Tukey studentized range procedure. With unequal sample sizes, in the sense of efficiency for confidence interval lengths and experimentwise error rates, the procedure is superior to the various generalized Tukey procedures.

• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.63-77
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• 2006

Maximum product of spacings estimator is proposed in this paper as a competent alternative of maximum likelihood estimator for the parameters of exponentiated-Weibull distribution, which does work even when the maximum likelihood estimator does not exist. In addition, a Bayes type estimator known as generalized maximum likelihood estimator is also obtained for both of the shape parameters of the aforesaid distribution. Though, the closed form solutions for these proposed estimators do not exist yet these can be obtained by simple appropriate numerical techniques. The relative performances of estimators are compared on the basis of their relative risk efficiencies obtained under symmetric and asymmetric losses. An example based on simulated data is considered for illustration.