• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.977-985
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• 2010

In this paper, we propose an approach to conduct partial sufficient dimension reduction with heavily unbalanced categorical predictors. For this, we consider integrated categorical predictors and investigate certain conditions that the integrated categorical predictor is fully informative to partial sufficient dimension reduction. For illustration, the proposed approach is implemented on optimal partial sliced inverse regression in simulation and data analysis.

• Journal of the Korean Society of Costume
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• v.42
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• pp.231-242
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• 1999

The purpose of this study was to classified the dimension of clothing involvement and the clothing loyalty of 256 male and 271 female college students in Taegu area. data was analyzed by frequency percentage mean factor analysis reliability test validity test correlation and ANOVA by using SPSS/pc. The results of this study were as follows; 1. the dimension of clothing involvement was classified into four factors such as clothing interest dimension clothing symbolism dimension clothing economics dimension and clothing individuality dimension. 2. In the relationship between brand loyalty and four factors of clothing involvement there was positive appearance involvement there was positive appearance in clothing interest clothing symbolism and clothing individuality with brand loyalty but negative appearance in clothing economics. The correlation between clothing interest dimension and clothing symbolism dimension clothing interest dimension and clothing individuality dimension clothing symbolism dimension and clothing economics dimension clothing symbolism dimension and clothing individuality dimension was positive. And there was no relation between clothing economics dimension and clothing individuality dimension clothing economics dimension and clothing interest dimension. 3. According to individual character females than males the group aged 18 to 20 and 24 to 27 than the group aged 21 to 23 showed more active tendency to the clothing involvement dimension and also highertendency to brand loyalty. The students with a major in humanities science than the students with a major in natural science and more expending consumers on clothes showed more active tendency to the clothing symbolism dimension and higher tendency to brand loyalty. 4. On the whole the attitude of consumers on clothes was very high in the clothing interest dimension common in the clothing individuality dimension and very low in the clothing economics dimension.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.107-115
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• 2012

Yoo and Cook (2007) developed an optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression and it is known that their method is asymptotically optimal and its test statistic has a chi-squared distribution asymptotically under the null hypothesis. To check the effect of dimension used in estimation on regression coefficients and the explanatory power of the conditional mean model in multivariate regression, we applied their method to several simulated data sets with various dimensions. A small simulation study showed that it is quite helpful to search for an appropriate dimension for a given data set if we use the asymptotic test for the dimension as well as results from the estimation with several dimensions simultaneously.

• The Research Journal of the Costume Culture
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• v.17 no.5
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• pp.866-877
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• 2009

The purpose of this study was to develop the dimension of hand pattern-making for Korean glove. The glove pattern-making has difficult problem in combination of anthropometric and engineering aspects. In addition, existing dimension data are not enough for glove pattern-making. Therefore, to develop the dimension for glove this study comprehensive list of candidate hand data was reviewed and the manufacturers(career over the 15 years) were interviewed on the method of glove. The result of comparing between the structures in hand and existing glove pattern, there draw deduction from follows. Pattern-making for glove need size of hand length, thumb length, index finger length, middle finger length, ring finger length, hand circumference, thumb-ring finger circumference and maximum hand thickness.

• Speech Sciences
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• v.10 no.2
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• pp.45-55
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• 2003

Strange attractor can be used as a presentation method for signal processing. Fractal dimension is well known method that extract features from attractor. Even though the method provides powerful capabilities for speech processing, there is drawback which should be solved in advance. Normally, the size of the raw signal should be long enough for processing if we use the fractal dimension method. However, in the area of connected-digits problem, normally, syllable or semi-syllable based processing is applied. In this case, there is no evidence that we have sufficient data or not to extract characteristics of attractor. This paper discusses the relationship between the size of the signal data and the calculation result of fractal dimension, and also discusses the efficient way to be applied to connected-digit recognition.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.205-215
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• 2021

Sufficient dimension reduction is useful dimension reduction tool in regression, and sliced inverse regression (Li, 1991) is one of the most popular sufficient dimension reduction methodologies. In spite of its popularity, it is known to be sensitive to the number of slices. To overcome this shortcoming, the so-called fused sliced inverse regression is proposed by Cook and Zhang (2014). Unfortunately, the two existing methods do not have the direction application to large p-small n regression, in which the dimension reduction is desperately needed. In this paper, we newly propose seeded sliced inverse regression and seeded fused sliced inverse regression to overcome this deficit by adopting iterative projection approach (Cook et al., 2007). Numerical studies are presented to study their asymptotic estimation behaviors, and real data analysis confirms their practical usefulness in high-dimensional data analysis.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• Journal of Veterinary Clinics
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• v.17 no.1
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• pp.187-193
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• 2000

Fifteen adult Korea Jin-do dogs were studied by echocardiography to obtain the basic data of the imaging planes and normal references ranges to the aorta and pulmonary artery internal dimension. Measurements of aortic root internal dimension(AOID) and right pulmonary artery internal dimension (RPAID) were made at modified pulmonary arteries level short-axis view and left ventricular outflow tract long-axis view. The aortic root internal dimension and right pulmonary artery internal dimension at modified pulmonary arteries level short-axis view were 18.7$\pm$1.3mm (mean$\pm$SD) and 10.1$\pm$0.8mm, respectively. And RPAID/AOID was 0.5$\pm$0.1mm. The aortic root internal dimension and right pulmonary artery internal dimension at left ventricular outflow tract long-axis view were 19.3$\pm$1.6 mm and 10.7$\pm$1.3mm, respectively. And RPAID/AOID was 0.5$\pm$0.1mm. These results indicate that modified pulmonary arteries level short-axis view is useful planes to examine the aortic root and pulmonary arteries, and aortic root internal dimension is significantly higher(40~50%)than the right pulmonary artery internal dimension. Therefore measurements of aortic root internal and right pulmonary artery internal dimension can be used for monitoring dilation of pulmonary artery.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.114-125
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• 1997

This paper presents a method that automatically recognizes dimension sets from the mechanical drawings, and that classifies 6 types dimension sets according to functional purpose. In the proposed method, the object and closed-loop symbols are separated from the character-free drawings. Then object lines and interpretation lines are vectorized. And, after recognizing dimension sets(consistings of arrowhead, shape line, tail lines, extension lines, text-string, and feature control frame), we classify recognized dimension sets as horizontal, vertical, angular, diametral, radial, and leader dimension sets. Finally the proposed method converts classified dimension sets into AutoCAD data by using AutoLisp language. By using the methods of geometric modeling, the proposed method readily recognized and classifies dimension sets from complex drawings. Experimetnal results are presented, which are obtained by applying the proposed method to drawings drawn in compliance with the KS drafting standard.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.513-521
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• 2018

The goal of sufficient dimension reduction (SDR) is to replace original p-dimensional predictors with a lower-dimensional linearly transformed predictor. The sliced inverse regression (SIR) (Li, Journal of the American Statistical Association, 86, 316-342, 1991) is one of the most popular SDR methods because of its applicability and simple implementation in practice. However, SIR may yield different dimension reduction results for different numbers of slices and despite its popularity, is a clear deficit for SIR. To overcome this, a fused sliced inverse regression was recently proposed. The study shows that the dimension-reduced predictors is robust to the numbers of the slices, but it does not investigate how robust its dimension determination is. This paper suggests a permutation dimension determination for the fused sliced inverse regression that is compared with SIR to investigate the robustness to the numbers of slices in the dimension determination. Numerical studies confirm this and a real data example is presented.