• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.19-33
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• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.501-514
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• 2023

When analyzing high dimensional data such as text data, if we input all the variables as explanatory variables, statistical learning procedures may suffer from over-fitting problems. Furthermore, computational efficiency can deteriorate with a large number of variables. Dimensionality reduction techniques such as feature selection or feature extraction are useful for dealing with these problems. The sparse principal component analysis (SPCA) is one of the regularized least squares methods which employs an elastic net-type objective function. The SPCA can be used to remove insignificant principal components and identify important variables from noisy observations. In this study, we propose a dimension reduction procedure for text data based on the SPCA. Applying the proposed procedure to real data, we find that the reduced feature set maintains sufficient information in text data while the size of the feature set is reduced by removing redundant variables. As a result, the proposed procedure can improve classification accuracy and computational efficiency, especially for some classifiers such as the k-nearest neighbors algorithm.

• Korean Journal of Optics and Photonics
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• v.31 no.3
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• pp.125-133
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• 2020

An innovative method for separating overlapping latent fingerprints, using laser-induced plasma spectroscopy (LIPS) combined with multivariate analysis, is reported in the current study. LIPS provides the capabilities of real-time analysis and high-speed scanning, as well as data regarding the chemical components of overlapping fingerprints. These spectra provide valuable chemical information for the forensic classification and reconstruction of overlapping latent fingerprints, by applying appropriate multivariate analysis. This study utilizes principal-component analysis (PCA) and partial-least-squares (PLS) techniques for the basis classification of four types of fingerprints from the LIPS spectra. The proposed method is successfully demonstrated through a classification example of four distinct latent fingerprints, using discrimination such as soft independent modeling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA). This demonstration develops an accuracy of more than 85% and is proven to be sufficiently robust. In addition, by laser-scanning analysis at a spatial interval of 125 ㎛, the overlapping fingerprints were separated as two-dimensional forms.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.201-211
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• 2013

This study compares Customer Satisfaction Index(CSI) and the weight for each dimension by applying various methods of weight calculation and attempts to suggest some implications. For the purpose, the study classified the methods of weight calculation into the subjective method and the statistical method. Constant sum scale was used for the subjective method, and the statistical method was again segmented into correlation analysis, principal component analysis, factor analysis, structural equation model. The findings showed that there is difference between the weights from the subjective method and the statistical method. The order of the weights by the analysis methods were classified with similar patterns. Besides, the weight for each dimension by different methods of weight calculation showed considerable deviation and revealed the difference of discrimination and stability among the dimensions. Lastly, the CSI calculated by various methods of weight calculation showed to be the highest in structural equation model, followed by in the order of regression analysis, correlation analysis, arithmetic mean, principal component analysis, constant sum scale and factor analysis. The CSI calculated by each method showed to have statistically significant difference.

• Journal of the Korean Chemical Society
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• v.46 no.4
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• pp.309-317
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• 2002

A spectrofluorimetric method for the simultaneous determination of amino acids (tryptophan and tyrosine) based on the application of multivariate calibration method such as principal component regression and partial least squares (PLS) to luminescence measurements has been studied. Emission spectra of synthetic mixtures of two amino acids were obtained at excitation wavelength of 257 ㎚. The calibration model in PCR and PLS was obtained from the spectral data in the range of 280-500 ㎚ for each standard of a calibration set of 32 standards, each containing different amounts of two amino acids. The relative standard error of prediction ($RSEP_a$) was obtained to assess the model goodness in quantifying each analyte in a validation set. The overall relative standard error of prediction ($RSEP_m$) for the mixture obtained from the results of a validation set, formed by 6 independent mixtures was also used to validate the present method.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.1
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• pp.31-37
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• 1993

To analyze and classify the varietal variation of alkali digestibility in detail, which is closely connected with the gelatinization temperature and physical characteristics of cooked rice, the patterns of alkali decomposition changed along the alkali concentration were investigated for thirty three Korean leading rice cultivars and new breeding lines(japonica : 25, Tongil-type:8) including five glutinous rice. Principal component analysis was used to condense the information and to classify rice materials according to decomposed reaction pattern at several levels of potassium hydroxide(KOH) concentration. Thirty three rice varieties were classified largely into four groups by the distribution on the plane of upper two principal component scores which contained above 92% of total informations. Group I was consisted of one variety, Dobongbyeo, which owned almost same strong resistance to alkali digestion at the range of 0.8% to 1.6% KOH solutions. Group II included three japonica and Tongil-type glutinous rice varieties, which revealed medium alkali digestion value(ADV) at 1.4% KOH solution and intermediate change in ADV from 0.8% to 1.6% KOH solutions. Most of Tongil-type and early-maturity japonica rice, which exhibited medium-high ADV at 1.4% of KOH concentration and large ADV difference between low and high alkali solutions, were contained in Group III. Group N included most of medium or medium-late-maturity japonica, which showed high ADV at 1.4% KOH and medium or intermediate-high ADV change between low and high alkali solutions. The 1st principal component indicated the average index of ADV through 0.8-1.6% KOH solutions and the 2nd principal component pointed out the factor related with ADV difference between low and high alkali solutions or regression coefficients of ADV change along with the KOH concentrations.

• Applied Chemistry for Engineering
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• v.30 no.5
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• pp.615-619
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• 2019

Statistical design of experiments was performed to optimize MOF-235 synthesis process. Concentrations of terephthalic acid (TPA), iron (III) chloride hexahydrate, N,N-dimethylformamide (DMF) and ethanol were important factors to develop the crystal structure of MOF-235. MOF-235 was synthesized with various concentrations of the listed chemicals above and the crystallinity was measured by XRD. The effect of the composition on the synthesis of MOF-235 was evaluated using a statistical analysis. For the variance analysis using F-test, the concentration of ethanol showed the greatest effect on the crystallinity and TPA the least influential. A regression model for predicting the crystallinity of MOF-235 was derived and the prediction results for two synthetic variables were presented using contour plots. Finally, the crystallinity was predicted by a mixture method with $FeCl_3$, ethanol and DMF.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.183-191
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• 2018

In this study a traffic volume dependent displacement estimation model for Gwangan Bridge was developed using bridge monitoring big data. Traffic volume data for four different vehicle types and the vertical displacement data in the central position of the Gwangan Bridge were used to develop and validate the estimation model. Two statistical estimation models were developed using multiple regression analysis (MRA) and principal component analysis (PCA). Estimation performance of those two models were compared with actual values. The results show that both the MRA and the PCA based models are successfully estimating the vertical displacement of Gwangan Bridge. Based on the results, it is concluded that the developed model can effectively be used to predict the traffic volume dependent displacement behavior of Gwangan Bridge.

• Analytical Science and Technology
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• v.11 no.6
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• pp.448-451
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• 1998

The feasibility of using FT-NIR (Fourier Transform Near Infrared) spectrometer to measure NaOH, $Na_2CO_3$ and $Na_2S$ concentration in a naphtha cracking process, and an outline of the method development to identify spectral feature of the hydroxide whose band is overlapped by a strong water absorption were demonstrated. For measuring NaOH, $Na_2CO_3$ and $Na_2S$, FT-NIR spectrometer is a rapid and possible alternative to the current titration method with a standard deviation of 0.1.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.193-197
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• 2006

본 연구는 감성 공학적 접근법을 사용하여, 벽지의 디자인 요소와 소비자의 감성과의 관계를 정량적으로 규명하는 것을 목표로 한다. 문헌조사, 인터뷰 전문가 의견 등을 종합하여, 총 13개의 주관적 감성 변수(6개의 시각적 변수, 7개의 촉각적 변수) 와 4개의 벽지 디자인 요소(color, texture pattern, embossing depth, gloss)들이 추출되었으며, 최종 목표 감성은 '고급감'으로 정하였다. 9점 척도와 100점 척도으로 구성된 설문지를 통하여, 28개의 샘플 벽지에 대해서 30명의 목표 고객들을 대상으로 감성 평가 실험을 실시하였고, 주성분 회귀 분석, 수량화 이론 등을 이용한 분석을 통하여, 소비자의 감성과 디자인 요소와의 관계를 정량적으로 분석했으며, 고급감을 향상시킬 수 있는 감성 변수 조합과 디자인 요소 조합을 규명하였다.