• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.233-241
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• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1151-1160
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• 2014

In DNA microarray studies, the number of genes far exceeds the number of samples and the gene expression measures are highly correlated. Partial least squares regression (PLSR) is one of the popular methods for dimensional reduction and known to be useful for the classifications of microarray data by several studies. In this study, we suggest a modified version of the partial least squares regression to analyze gene expression data with survival information. The method is designed as a new gene selection method using PLSR with an iterative procedure of imputing censored survival time. Mean square error of prediction criterion is used to determine the dimension of the model. To visualize the data, plot for variables superimposed with samples are used. The method is applied to two microarray data sets, both containing survival time. The results show that the proposed method works well for interpreting gene expression microarray data.

• 한국지능시스템학회논문지
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• 제13권5호
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• pp.571-575
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• 2003

In this paper, we describe a method for fuzzy polynomial regression analysis for fuzzy input--output data using shape preserving operations for least-squares fitting. Shape preserving operations simplifies the computation of fuzzy arithmetic operations. We derive the solution using mixed nonlinear program.

• 한국시뮬레이션학회논문지
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• 제9권3호
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• pp.43-51
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• 2000

In regression model, we estimate the unknown parameters by using various methods. There are the least squares method which is the most general, the least absolute deviation method, the regression quantile method and the asymmetric least squares method. In this paper, we will compare each others with two cases: firstly the theoretical comparison in the asymptotic sense and then the practical comparison using Monte Carlo simulation for a small sample size.

• Archives of Pharmacal Research
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• 제28권5호
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• pp.598-603
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• 2005

The purpose of this study was to determine the influence of weight with gentamicin assay error on the Bayesian and nonlinear least squares regression analysis in 12 Korean appen dicitis patients. Gentamicin was administered intravenously over 0.5 h every 8 h. Three specimens were collected at 48 h after the first dose from all patients at the following times, just before regularly scheduled infusion, at 0.5 h and 2 h after the end of 0.5 h infusion. Serum gentamicin levels were analyzed by fluorescence polarization immunoassay technique with TDxFLx. The standard deviation (SD) of the assay over its working range had been determined at the serum gentamicin concentrations of 0, 2, 4, 8, 12, and 16 ${\mu}g$/mL in quadruplicate. The polynominal equation of gentamicin assay error was found to be SD (${\mu}g$/mL) = 0.0246-(0.0495C)+ (0.00203C$^2$). There were differences in the influence of weight with gentamicin assay error on pharmacokinetic parameters of gentamicin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynominal equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result would be improved dosage regimens and better, safer care of patients receiving gentamicin.

• Communications for Statistical Applications and Methods
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• 제17권2호
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.931-940
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• 2011

분류분석은 학습표본으로부터 분류규칙을 도출한 후 새로운 표본에 적용하여 특정 범주로 분류하는 방법이다. 데이터의 복잡성에 따라 다양한 분류분석 방법이 개발되어 왔지만, 데이터 차원이 높고 변수간 상관성이 높은 경우 정확하게 분류하는 것은 쉽지 않다. 본 연구에서는 데이터차원이 상대적으로 높고 변수간 상관성이 높을 때 강건한 분류방법을 제안하고자 한다. 부분최소자승법은 연속형데이터에 사용되는 기법으로서 고차원이면서 독립변수간 상관성이 높을 때 예측력이 높은 통계기법으로 알려져 있는 다변량 분석기법이다. 벌점 부분최소자승법을 이용한 분류방법을 실제데이터와 시뮬레이션을 적용하여 성능을 비교하고자 한다.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• 대한인간공학회지
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• 제20권1호
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• pp.45-62
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• 2001

This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

• Structural Engineering and Mechanics
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• 제31권1호
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.