• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.245-253
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• 2011

Powers of rank transformed statistic for testing main effects and interaction effects for $2{\times}2$ factorial design in randomized complete block design are very superior to powers of parametric statistic without regard to the block size, composition method of effects and the type of population distributions such as exponential, double exponential, normal and uniform. $2{\times}2$ factorial design in RCBD increases error effects and decreases powers of parametric statistic which results in conservativeness. However powers of rank transformed statistic maintain relative preference. In general powers of rank transformed statistic show relative preference over those of parametric statistic with small block size and big effect size.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.1013-1022
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• 2003

This paper implemented the real-time object tracking algorithm that extracts and tracks the moving object adaptively to input frame sequence by using non-parametric image processing method and Kalman filter-based dynamic AR(2) process method. By applying non-parametric image processing to input frames, the moving object was extracted from the background adaptively to diverse environmental conditions. And the movement of object was able to be adaptively estimated and tracked by modeling the various movement of object as dynamic AR(2) process and estimating based on the Kalman filter the parameters of AR(2) process dynamically changing along time. The experiments of the implemented object tracking system showed that the proposed method tracked the moving object as more approximately as the estimation error became about l/2.5∼1/50 of one of the traditional tracking method based on linear Kalman filter.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.437-465
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• 2019

In this study, we propose a new method to forecast long-term energy demand in Korea. Based on Chang et al. (2016), which models the time varying long-run relationship between electricity demand and GDP with a function coefficient panel model, we design several schemes to retain objectivity of the forecasting model. First, we select the bandwidth parameters for the income coefficient based on the out-of-sample forecasting performance. Second, we extend the income coefficient using the functional principal component analysis method. Third, we proposed a method to reflect the elasticity change patterns inherent in Korea. In the empirical analysis part, we forecasts the long-term energy demand in Korea using the proposed method to show that the proposed method generates more stable long term forecasts than the existing methods.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.143-152
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• 2017

Restriction of completely randomization within a block can be handled by a split plot factorial design splitted by several plots. $3{\times}3$ split plot factorial design with two fixed main factors and one fixed block shows that powers of the rank transformed statistic for testing whole plot factorial effect and split plot factorial effect are superior to those of the parametric statistic when existing effect size is small or the remaining effect size is relatively smaller than the testing factorial effect size. Powers of the rank transformed statistic show relatively high level for exponential and double exponential distributions, whereas powers of the parametric and rank transformed statistic maintain similar level for normal and uniform distributions. Powers of the parametric and rank transformed statistic with two fixed main factors and one random block are respectively lower than those with all fixed factors. Powers of the parametric andrank transformed statistic for testing split plot factorial effect with two fixed main factors and one random block are slightly lower than those for testing whole plot factorial effect, but powers of the rank transformed statistic show comparative advantage over those of the parametric statistic.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.327-340
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• 2008

This paper derives joint and conditional Lagrange multiplier tests based on information matrix for testing functional form and/or the presence of autocorrelation in a regression model. Small sample properties of these tests are assessed by Monte Carlo study and comparisons are made with LM tests based on Hessian matrix. The results show that the proposed $LM_E$ tests have the most appropriate finite sample performance.

• Journal of Animal Science and Technology
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• v.45 no.4
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• pp.509-516
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• 2003

Growth curves were estimated for 1083 female Korean cattle raised in Daekwanryeong branch, National Livestock Research Institute (NLRI). Comparisons were made among various growth curve models for goodness of fit for the growth of the cows. Estimated growth curve functions were $W_t=370.2e^{-2.208e^{-0.00327t}$ for Gompertz model, for von Bertalanffy model, and $W_t=341.2(1+5.652e^{-0.00524t})^{-1}$ for Logistic model. Ages at inflection estimated from Gompertz model, von Bertalanffy model and Logistic model were 242.2 days, 191.5 days, and 330.5 days respectively, body weight at inflection were 136kg, 115kg, and 170kg, and daily gain at inflection were 0.445kg, 0.451kg, and 0.446kg. The predicted weights by ages from Gompertz model, von Bertalanffy model, and Logistic model were onsistently overestimated at birth weight and underestimated at 36 month weight. The von Bertalanffy model which had a variable point of inflection fit the data best.

• The Korean Journal of Financial Management
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• v.26 no.2
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• pp.155-180
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• 2009

This study is to estimate and compare hedging effectiveness in emerging currency and advanced currency futures markets. Emerging currency futures includes Korea won, Mexico peso, and Brazil real and advanced currency futures is Europe euro, British pound, and Japan yen. Hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS model, error correction model and constant condintional correlation bivariate GARCH(1, 1) hedge model based on rolling windows. Analysis data is used daily spot and futures rates from January, 2, 2001 to March. 10, 2006. The empirical results are summarized as follows : First, irrespective of hedging period and model, hedging using Korea won/dollar futures reduces spot rate's volatility risk by 97%. Second, Korea won/dollar futures market produces the best hedging performance in emerging and advanced currency futures markets, i.e. Mexico peso, Brazil real, Europe euro, British pound, and Japan yen. Third, there are no difference of hedging effectiveness among hedging models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.11
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• pp.2421-2428
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• 2002

In this study, the failure probability of the degraded LP turbine blade steel was performed using the Monte Carlo simulation to apply variation of applied stress and strength. For this purpose, applied stress under the service condition of steady state was obtained by theoretical stress analysis and the maximum Von-Mises stress was 219MPa. The fatigue strength under rotating-bending load was evaluated by the staircase method. Furthermore, 3-parameter Weibull distribution was found to be most appropriate among assumed distributions when the probabilistic distributions of tensile and fatigue strength were determined by the proposed analysis. The failure probability with various loading conditions was derived from the strength-stress interference model and the characteristic factor of safety was also estimated.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.265-275
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• 2009

This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.723-730
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• 2009

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001 ${\sim}$ 2006 are used, and results from two methods are compared with using SSF(Sum of square for forecasting error).