• Journal of the Chungcheong Mathematical Society
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• v.21 no.1
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• pp.29-48
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• 2008

We deal with the classification problem of real hypersurfaces in a complex hyperbolic space. In order to classify real hypersurfaces in a complex hyperbolic space we characterize a real hypersurface M in $H_n(\mathbb{C})$ whose structure vector field is not principal. We also construct extrinsically homogeneous real hypersurfaces with four distinct curvatures and their structure vector fields are not principal.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.533-541
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• 2013

In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.4
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• pp.33-40
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• 2006

In present study, the principal stress condition needed to conduct cubical large-scale triaxial test which can reflect three dimensional stress condition (or plain strain condition) in a dam was investigated by performing model test and numerical analysis and the principal stress ratio varying with the height of CFRD was examined. Also, the principal stress ratio in CFRD body was investigated from the monitoring results of horizontal and vertical earth pressure gages, installed in the center zone and lower part of transition zone of the dam body, respectively, in order to consider the principal stress condition in the large-scale triaxial test to model the behavior of CFRD. The result of the study indicated that the principal stress ratio decreased gradually from the lower to the upper part in the dam body for its center axis and was about 0.5 and 0.2 in the lower and upper part, respectively.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.311-321
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• 2017

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Herbal Formula Science
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• v.11 no.2
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• pp.1-18
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• 2003

The Theory for Monarch, Minister, Adjuvant and Dispatcher (or the Theory of Principal, Assistant, Adjuvant and Guiding Korean Oriental Herbal Medicines) has served as a standard principle for newly developed prescription formulas as well as established ones. Despite its significance, however, this theory hasn't been thoroughly studied and covered in the academic journals of Korean Oriental Herbal Medicines (KOHM) yet. This paper inquires into the origin of the theory while presenting the definitions and functions of Principal, Assistant, Adjuvant, and Guiding KOHM. In the end, the recommended doses and number of the KOHM comprising each of Principal, Assistant, Adjuvant, and Guiding KOHM are suggested. The compatibility theory of Principal, Assistant, Adjuvant, and Guiding KOHM can be traced back to the Warring States Period during which it was recorded in the treatise of the various schools of thoughts and their exponents. The theory was firmly established as a full system in ${\ulcorner}Shinnong's\;Pharmacopoeia{\lrcorner}\;and\;{\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$. While ${\ulcorner}Shinnong's\;Pharmacopoeia{\lrcorner}$ focuses on the classification of the properties of KOHM, ${\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$ mainly deals with the principles for writing prescriptions. In this regard, it is ${\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$ that systemized the Theory of Principal, Assistant, Adjuvant, and Guiding KOHM in a real sense. Principal KOHM aims at the causes of diseases and treat main symptoms. The doses are greater than Assistant, Adjuvant and Guiding KOHM. With their comprehensive effects, Principal KOHM is a leading ingredient of any prescription formula. Assistant KOHM are similar to Principal KOHM in its natures and flavors. Although its natures, flavors as well as efficacies may slightly differ from those of Principal KOHM, Assistant KOHM strengthens the therapeutic effects, jointly working with Principal KOHM. They mainly treat accompanying diseases and symptoms. Adjuvant KOHM is divided into two types: facilitator and inhibitor. Facilitators with the similar properties to those of Principal and Assistant KOHM help strengthen the therapeutic effects. Since they usually treat accompanying symptoms or secondary accompanying symptoms (minor accompanying symptoms), there are two kinds of facilitators. (1) The first kind of facilitators assists Principal KOHM, targeting accompanying symptoms. (2) The second ones supporting Assistant KOHM are for accompanying or secondary accompanying symptoms (or minor accompanying symptoms). Inhibitors counteract and thereby complement Principal and Assistant KOHM. Some of them inhibit the side effects or toxicity of Principal KOHM for the sake of the safety of the whole prescription formula while the others generate induced interactions. Guiding KOHM can be used for two purposes: guiding and mediating. The Guiding KOHM for the former purpose leads the other KOHM in a prescription formula to the lesion. But, the Guiding KOHM for mediating coodinate and harmonize all the ingredients in a prescription formula. The number of KOHM for those Principal, Assistant, Adjuvant and Guiding KOHM and their doses are different, depending on the types of prescriptions: classical prescriptions, prescriptions after ${\ulcorner}$Treatise of Cold-Induced Diseases${\lrcorner}$ and prescriptions of Sasang Constitutions Medicines. In the case of the prescriptions after ${\ulcorner}$Treatise of Cold-Induced Diseases${\lrcorner}$, it is highly recommended to follow the view of ${\ulcorner}$Thesaurus of Korean Oriental Medicine Doctors in Chosun Dynasty${\lrcorner}$ for the number of KOHM to be used. For the doses, however, ${\ulcorner}$Elementary Course for Medicine${\lrcorner}$, is found to be more accurate. The most appropriate number of KOHM per prescription is 11-13. To be more specific, for one prescription formula, it is recommended to administer one kind of KOHM for Principal KOHM, 2-3 for Assistant KOHM, 3-4 for Adjuvant KOHM and 5 for Guiding KOHM. As for the proportion of the doses, when 10 units are to be administered for Principal KOHM in a formula, the doses for the other three should be 7-8 units for Assistant KOHM, 5-6 for Adjuvant KOHM and 3-4 for Guiding KOHM. The doses of the KOHM added to or taken out of the prescription correspond to those of Adjuvant and Guiding KOHM.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1025-1036
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• 2003

Variable selection algorithm for principal component analysis using penalty function is proposed. We use the fact that usual principal component problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Simulated annealing algorithm is used in searching for optimal solutions with penalty functions. Comparisons between several well-known penalty functions through simulation reveals that the HARD penalty function should be suggested as the best one in several aspects. Illustrations with real and simulated examples are provided.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.623-630
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• 2003

Recently, Tanaka(1988) derived two influence functions related to an eigenvalue problem $(A-\lambda_sI)\upsilon_s=0$ of real symmetric matrix A and used them for sensitivity analysis in principal component analysis. In this paper, we deal with the perturbation expansions up to quadratic terms of the same functions and discuss the application to sensitivity analysis in principal component regression analysis(PCRA). Numerical example is given to show how the approximation improves with the quadratic term.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.151-156
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• 2003

Variable selection algorithm for principal component analysis using penalized likelihood method is proposed. We will adopt a probabilistic principal component idea to utilize likelihood function for the problem and use HARD penalty function to force coefficients of any irrelevant variables for each component to zero. Consistency and sparsity of coefficient estimates will be provided with results of small simulated and illustrative real examples.

• Kyungpook Mathematical Journal
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• v.63 no.4
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• pp.571-575
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• 2023

Let D be an integral domain and let * be a star-operation on D. In this article, we give new characterizations of *_w-Noetherian domains and *_w-principal ideal domains. More precisely, we show that D is a *_w-Noetherian domain (resp., *_w-principal ideal domain) if and only if every *_w-countable type ideal of D is of *_w-finite type (resp., principal).