• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.17-25
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• 2000

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.739-745
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• 2010

This paper computes the repurchase index based on statistical modeling. Using the transaction record of a certain product, the repurchase index is obtained by fitting the Poisson regression model. The customers are classified into 5 groups based on the index giving the information about the propensity to repurchase.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.233-244
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• 2004

The influence of observations is investigated in fitting proportional covariance matrices model. Local influence measures are obtained when all parameters or subsets of the parameters are of interest. We will also derive the local influence measure for investigating the influence of observations in testing the proportionality of covariance matrices. A numerical example is given for illustration.

• Advances in Energy Research
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• v.1 no.1
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• pp.35-52
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• 2013

A model for DMFC anode performance is developed. The model takes into account potential--independent methanol adsorption on the catalyst surface, finite rate of proton transport through the anode catalyst layer (ACL), and a potential loss due to methanol transport in the anode backing layer. An approximate analytical half--cell polarization curve is derived and equations for the anode limiting current density are obtained. The polarization curve is fitted to the curves measured by Nordlund and Lindbergh and parameters resulted from the fitting are discussed.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.177-183
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• 1997

A data-adaptive order selection procedure is proposed for local polynomial nonparametric regression. For each given polynomial order, bias and variance are estimated and the adaptive polynomial order that has the smallest estimated mean squared error is selected locally at each location point. To estimate mean squared error, empirical bias estimate of Ruppert (1995) and local polynomial variance estimate of Ruppert, Wand, Wand, Holst and Hossjer (1995) are used. Since the proposed method does not require fitting polynomial model of order higher than the model order, it is simpler than the order selection method proposed by Fan and Gijbels (1995b).

• Electrical & Electronic Materials
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• v.6 no.1
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• pp.40-49
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• 1993

본 논문은 fitting 파라미커를 배제하고 2차원적 Poisson 방식을 도출해서 Submicron MOSFET의 model식을 완전히 해석적으로 성립시켰다. 이로 인해 포화영역, 문턱전압, 강반전에 대한 것이 동시에 표현되는 정확한 드레인 전류가 유도되었다. 더욱이 이 model은 short-channel과 body효과, DIBL효과, 그리고 carrier운동에 대한 것도 설명할 수 있으며 온도와 n$^{+}$접합, 산화층에 관련되는 문턱전압도 표현할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.847-857
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• 2007

This paper provides a statistical algorithm which detects COD (chemical oxygen demand) measurement machine error on real-time. For this we propose to use regression model fitting and check its validity against the current observations. The main idea is that the normal regression relation between COD measurement and other parameters inside the machine will be violated when the machine is out of order.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.531-541
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• 1997

The asymptotic distribution of residual autocorrelation functions from a generalized p-order random coefficient autoregressive process (GRCA(p)) is derived. To this end, we first describe the GRCA(p) models and then consider the normalised residuals after fitting the model. This result can be applied to the residual analysis for the diagonostic purpose.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.158-161
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• 2002

Many methods were proposed for insulation degradation diagnosis to High voltage and capacity Transformer in live. IDD is difficult by those methods because insulation degradation circumstances and characteristics of electrical plant are different with other Therefore, it is necessary to design diagnosis algorithms fitting for each. In this paper, We develop IIDS that used diagnosis algorithm with fuzzy model and hardware with MCU.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.429-442
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• 2015

Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.