• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.557-573
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• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.179-193
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• 1995

A multi-objective, single-stage scheduling problem with stochastic processing times is considered where the objective is to simultaneously minimize the expected value and the variance of total flowtime, and the mean probability of tardiness. In cases where processing times follow normal distributions, a method using pairwise interchange of two jobs(PITJ) is proposed to generate a set of the approximate efficient schedules. The efficient schedules are not dominated by the criterion vectors of any other permutation schdules in the feasible region. Numerical experiments performed to ascertain the effectiveness of PITJ algorithm are also reported in the results.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.101-109
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• 1996

네트웍 샘플링은 회귀한 속성을 갖는 모집단에서 유용한 표본조사방법이다. 기존의 중복수 추정량(multiplicity estimator)은 네트웍 샘플링의 특징을 반영하는 추정량으로 응답오차를 고려하지 않은 경우에 이용되었다. 본 논문에서는 응답오차를 고려한 경우와 이용할 수 있는 수정된 중복수 추정량을 제안하였다. 그리고 제안된 추정량의 기대값과 근사기대분산(approximate expexted variance)을 유도하였으며, 제안된 추정량이 기존의 모총수 추정량보다 화과적임을 가상모집단을 통하여 보였다.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.793-802
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• 2012

One proposal is made to construct a nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of the idea of minimizing approximate variance of a proposed estimator using regression quantiles. This nonparametric estimator and some other L-estimators are studied and compared with well known M-estimators through a simulation study.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.345-351
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• 1997

The motion of an aircraft landing gear over rough runway at variable speed is nonstationary. hi this paper, a method for the computation of nonstationary response variance is presented which uses a state space form for the combination of landing gear and runway excitation. The dynamic characteristics of the landing gear under nonstationazy random excitations has also been analyzed using the proposed method. The formulation is for linear systems of arbitrary order and allows any deterministic velocity history.

• Bulletin of the Korean Chemical Society
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• v.16 no.11
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• pp.1113-1118
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• 1995

The Schlo'gl model with the first order transition for a photochemical reaction is considered to study the dynamic behaviors in the neighborhood of the Gaussian white noise by obtaining the explicit results of the time-dependent variance and time correlation function with the aid of approximate methods based on the stationary properties of the system. Then, we discuss the effect of external noise strength on the stability of the model at steady states in detail.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.13-24
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• 2001

In this paper, we investigated mean estimation methods in two-phase sampling. Under the fixed expected cost we reviewed the optimal sample sizes, minimum variances and approximate unbiased variance estimators for usual ratio estimator, stratified sample mean with proportional allocation and Rao's allocation of the second phase sample. Also we proposed combined ratio estimator, which uses both ratio estimation and stratification and derived optimal sample size, minimum variance and unbiased variance estimator. Through a limited simulation study, we compared estimators by design effects and came to know that ratio estimator is more efficient than stratified sample mean in some cases and inefficient in the other cases, but combined ratio estimator is more efficient than others in most cases.

• The Mathematical Education
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• v.45 no.2 s.113
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• pp.145-154
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• 2006

The main purpose of this study is to investigate the effect of departures from normality and equal variance on the two-sample test when the variances are unknown. We have found that type I error brought about a little bit change which is ignorable in relation to kurtosis. But the change of type I error was mainly based on the skewness of the parent population. In introductory statistics classes where data analysis includes techniques for detecting skewness of two populations, we recommend the two-sample t-test when maximal skewness of two populations is smalter than the value 4 when the variances seem equal. Furthermore, our simulations reveal that the two-sample t-test appears somewhat more robust than that of z-test if the assumption of equal variance is satisfied. In the case of unequal variance, the two-sample t-test appears somewhat more robust provided the t-statistic using Satterthwaite's approximate degrees of freedom.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.549-554
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• 2012

In this paper, we consider the optimal fuzzy filter of nonlinear discrete-time with estimation error variance constraint. First, the Takagi and Sugeno(T-S) fuzzy model is employed to approximate the nonlinear system. Next, the error state is mean square bounded, and the steady state variance of the estimation error of each state is not more than the individual predefined value. It is shown that, the addressed problem can be carried out by solving linear matrix inequality(LMI) and some algebraic quadratic matrix inequalities. Finally, some examples are provided to illustrate the design procedure and expected performance through simulations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.