Bu-Yong Kim

ATHB12, an ABA-Inducible Homeodomain-Leucine Zipper (HD-Zip) Protein of Arabidopsis, Negatively Regulates the Growth of the Inflorescence Stem by Decreasing the Expression of a Gibberellin 20-Oxidase Gene

Arabidopsis thaliana homeobox 12 (ATHB12) is rapidly induced by ABA and water stress. A T-DNA insertion mutant of ATHB12 with a reduced level of ATHB12 expression in stems had longer inflorescence stems and reduced sensitivity to ABA during germination. A high level of transcripts of gibberellin 20-oxidase 1 (GA20ox1), a key enzyme in the synthesis of gibberellins, was detected in athb12 stems, while transgenic lines overexpressing ATHB12 (A12OX) had a reduced level of GA20ox1 in stems. Consistent with these data, ABA treatment of wild-type plants resulted in decreased GA20ox1 expression whereas ABA treatment of the athb12 mutant gave rise to slightly decreased GA20ox1 expression. Retarded stem growth in 3-week-old A12OX plants was rescued by exogenous GA(9), but not by GA(12), and less GA(9) was detected in A12OX stems than in wild-type stems. These data imply that ATHB12 decreases GA20ox1 expression in stems. On the other hand, the stems of A12OX plants grew rapidly after the first 3 weeks, so that they were almost as high as wild-type plants at about 5 weeks after germination. We also found changes in the stems of transgenic plants overexpressing ATHB12, such as alterations of expression GA20ox and GA3ox genes, and of GA(4) levels, which appear to result from feedback regulation. Repression of GA20ox1 by ATHB12 was confirmed by transfection of leaf protoplasts. ABA-treated protoplasts also showed increased ATHB12 expression and reduced GA20ox1 expression. These findings all suggest that ATHB12 negatively regulates the expression of a GA 20-oxidase gene in inflorescence stems.

New Design of Choice Sets for Choice-based Conjoint Analysis

Abstract This article is concerned with choice-based conjoint analysis versus rating-based and ranking-based conjointanalysis. Choice-based conjoint analysis has a definite advantage in that the respondent’s task of choosing themost preferred profile from several competing profiles adequately mimics consumer marketplace behavior. Itis crucial to design the choice sets appropriate for the choice-based conjoint. Thus, this article suggests a newmethod to design the choice sets that are well-balanced. It augments the balanced incomplete block designand then obtains the dual design of the result to accommodate various numbers of profiles. In consequence,the choice sets designed by the new method have the desirable characteristics that each profile is presentedto the same number of respondents, and pairs of any two distinct profiles occur together in the same numberof choice sets. The balancing of the design increases the efficiency of the conjoint analysis. In addition,the pair-comparison scheme can improve the quality of data through the identification of contradictoryresponses.Keywords: Choice-based conjoint analysis, choice set, balanced incomplete block design, dual design.

Analysis of Consumer Preferences for Cosmetic Essence-for-Men via Choice-Based Conjoint with New Design of Choice Sets

The sales volume of mens cosmetics has drastically increased in Korea. In recent years, mens needs for cosmetics have been diversified and the consumer demand for functional cosmetics has greatly risen. In particular, male consumers have become more interested in essence product that is a light and concentrated treatment to correct skin problems. This research analyzes consumer preferences for essence-for-men through the use of choice-based conjoint analysis. This approach is adopted since the task of respondents to choose the most preferred option from several alternatives closely mimics actual marketplace purchasing behavior by consumers. New technique for the construction of choice sets is suggested based on the balanced incomplete block design, to accommodate a larger number of product profiles. The proposed design for choice sets is balanced and provides a tool to filter the contradictory choices. Conjoint analyses are performed to assess the relative importance of attributes and identify the most preferred profile of essence-for-men with respect to attributes such as emphasized function, price, type of content, and design of container. Some differences are indicated in the analysis results between age brackets as well as between groups classified by the amount of fashion item expenditures.

Procedure for the Selection of Principal Components in Principal Components Regression

Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are larger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.

Principal Components Logistic Regression based on Robust Estimation

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

Identification of Regression Outliers Based on Clustering of LMS-residual Plots

An algorithm is proposed to identify multiple outliers in linear regression. It is based on the clustering of residuals from the least median of squares estimation. A cut-height criterion for the hierarchical cluster tree is suggested, which yields the optimal clustering of the regression outliers. Comparisons of the effectiveness of the procedures are performed on the basis of the classic data and artificial data sets, and it is shown that the proposed algorithm is superior to the one that is based on the least squares estimation. In particular, the algorithm deals very well with the masking and swamping effects while the other does not.

A Criterion for the Selection of Principal Components in the Robust Principal Component Regression

Robust principal components regression is suggested to deal with both the multicollinearity and outlier problem. A main aspect of the robust principal components regression is the selection of an optimal set of principal components. Instead of the eigenvalue of the sample covariance matrix, a selection criterion is developed based on the condition index of the minimum volume ellipsoid estimator which is highly robust against leverage points. In addition, the least trimmed squares estimation is employed to cope with regression outliers. Monte Carlo simulation results indicate that the proposed criterion is superior to existing ones.

Three Dimensional CERES Plot in Generalized Linear Models

We explore the structure and usefulness of three dimensional CERES plot as a basic tool for dealing with curvature as a function of the new predictors in generalized linear models. If predictors have nonlinear effects and there are nonlinear relationships among the predictors, the partial residual plot is not able to display the correct functional form of the predictors. Unlike this plots, the CERES plot can show the correct form. This is illustrated by simulated data.

Principal Components Regression in Logistic Model

The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.

Algorithm for the Robust Estimation in Logistic Regression

The maximum likelihood estimation is not robust against outliers in the logistic regression. Thus we propose an algorithm for the robust estimation, which identifies the bad leverage points and vertical outliers by the V-mask type criterion, and then strives to dampen the effect of outliers. Our main finding is that, by an appropriate selection of weights and factors, we could obtain the logistic estimates with high breakdown point. The proposed algorithm is evaluated by means of the correct classification rate on the basis of real-life and artificial data sets. The results indicate that the proposed algorithm is superior to the maximum likelihood estimation in terms of the classification.