• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.393-401
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• 2012

This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.11-18
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• 2003

Quite often normality assumptions are not satisfied in practical applications. In this paper, an estimated generalized least squares(EGLS) analysis are considered in two way mixed linear models with arbitrary types of distributions for random effects. We investigate the power performance of EGLS analysis based on Henderson's method III, ML, REML and MINQUE(1). The power performances depend on the imbalance of design, on the actual values of ratio of variance components, and on the skewness and kurtosis parameters of the underlying distributions slightly. Results of our limited simulation study suggest that the EGLS F-statistics using four estimators and arbitrary distributions produce similar type I error rates and power performance.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.219-225
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• 1998

The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

• The Journal of Fisheries Business Administration
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• v.42 no.2
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• pp.69-83
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• 2011

This paper estimates demand functions of oyster as Kimchi's ingredients of capital area, other areas excluding a capital area, and a whole area in Korea to forecast its demand quantities in 2011~2015. To estimate oyster demand function, this paper uses pooled data produced from Korean housewives over 30 years old in 2009 and 2010. Also, this paper adopts several econometrics methods such as Ordinary Least Squares and Feasible Generalized Least Squares. First of all, to choose appropriate variables of oyster demand functions by area, this paper carries out model's specification with joint significance test. Secondly, to remedy heteroscedasticity with pooled data, this paper attempts residual plotting between estimated squared residuals and estimated dependent variable and then, if it happens, undertakes White test to care the problem. Thirdly, to test multicollinearity between variables with pooled data, this paper checks correlations between variables by area. In this analysis, oyster demand functions of a capital area and a whole area need price of the oyster, price of the cabbage for Gimjang, and income as independent variables. The function on other areas excluding a capital area only needs price of the oyster and income as ones. In addition, the oyster demand function of a whole area needed White test to care a heteroscedasticity problem and demand functions of the other two regions did not have the problem. Thus, first model was estimated by FGLS and second two models were carried out by OLS. The results suggest that oyster demand quantities per a household as Kimchi's ingredients are going to slightly increase in a capital area and a whole area, but slightly decrease in other areas excluding a capital area in 2011~2015. Also, the results show that oyster demand quantities as kimchi's ingredients for total household targeting housewives over 30 years old are going to slightly increase in three areas in 2011~2015.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.59-64
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• 1996

When the input-output record is available, the identification of a bilinear system is considered. It is assumed that the input is noise free and the output is contaminated by an additive noise. It is further assumed that the covariance matrix of the noise is known up to a factor of proportionality. The extended generalized total least squares (e-GTLS) method is proposed as one of the consistent estimators of the bilinear system parameters. Considering that the input is noise-free and that bilinear system equation is linear with respect to the system parameters, we extend the GTLS problem. The extended GTLS problem is reduced to an unconstrained minimization problem, and is solved by the Newton-Raphson method. We compare the GTLS method and the e-GTLS method in the point of the accuracy of the estimated system parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.176-181
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• 1992

In this paper, an adaptive generalized predictive control(GPC) algorithm which minimizes a N-stage cost function is proposed. The resulting controller is based on GPC algorithm and can be used in unknown plant parameters as the parameters of one step ahead predictor are estimated by recursive least squares method. The estimated parameters are extended to G,P, and F amtrix which contain the parameters of N step ahead predictors. And the minimization of cost function assuming no constraints on future controls results in the projected control increment vector. Hence this adaptive GPC algorithm can be used for either unknown system or varing system parameters, and it is also shown through simulations that the algorithm is robust to the variation of system parameters. This adaptive GPC scheme is shown to have the same stability properties as the deterministic GPC, and requires small amount of calculation compared to other adaptive algorithms which minimize N-stage cost function. Especially, in case that the maximum output horizon is 1, the proposed algorithm can be applicable to direct adaptive GPC.

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.32-37
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• 2000

In an industrial process, the proper objective is to find the optimal operating conditions with minimum process variability around the target. Vining and Myers(1990) suggest to use the separate model for the mean response and the process varian linear predictor ${\tau}_i={\log}\;{\sigma}^2_i$ is unknown and should be estimated. Noting that the variance of $\hat{{\tau}_i}$ is heterogeneous, another appropriate D-optimality criterion $D_3$ based on the method of generalized least squares is proposed in this paper.

• Journal of the Korean Operations Research and Management Science Society
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• v.8 no.2
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• pp.45-56
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• 1983

This paper presents a generalized fuel choice model in which restrictive constraints on cross-price coefficients as Baughman-Joskow-FEA Logit Model need not be imposed, but all demand elasticities are uniquely determined. The model is applied to estimating aggregate energy demand and fuel choices for the residential and commercial sector. The structural equations are estimated by a generalized least squares procedure using national-level EPB, KDI, BK, KRIS, MOER data for 1965 and 1980, and other related reports. The econometric results support the argument that “third-price” and “fourth-price” coefficients should not be constrained in estimating relative market share models. Furthermore, by using this fuel choice model, it has forecasted energy demands by fuel sources in, the residential and commercial sector until 1991. The results are turned out good estimates to compare with existing demands forecasted from other institutes.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.124-131
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• 2016

Baseline correction is very important due to influence on performance of spectral analysis in application of spectroscopy. Baseline is often estimated by parameter selection using visual inspection on analyte spectrum. It is a highly subjective procedure and can be tedious work especially with a large number of data. For these reasons, it is an objective and automatic procedure is necessary to select optimal parameter value for baseline correction. Asymmetrically reweighted penalized least squares (arPLS) based on penalized least squares was proposed for baseline correction in our previous study. The method uses a new weighting scheme based on the generalized logistic function. In this study, we present an automatic selection of optimal parameter for baseline correction using arPLS. The method computes fitness and smoothness values of fitted baseline within available range of parameters and then selects optimal parameter when the sum of normalized fitness and smoothness gets minimum. According to the experimental results using simulated data with varying baselines, sloping, curved and doubly curved baseline, and real Raman spectra, we confirmed that the proposed method can be effectively applied to optimal parameter selection for baseline correction using arPLS.