• Macromolecular Research
• /
• v.16 no.1
• /
• pp.51-56
• /
• 2008

Spherical micelles of poly(styrene-b-isoprene) (SI) diblock copolymers in selective solvents have been reported to pack onto either face-centered cubic (fcc) or body-centered cubic (bcc) lattices. The selection rule for fcc and bcc lattices has been understood in terms of the intermicellar potentials, and they have been quantified using the ratio of the corona layer thickness to the core radius, $L/R_c$, as suggested by McConnell and Gast. In order to test the validity of the McConnell-Gast criterion, this study compared the $L/R_c$ values from various solutions i.e. nine SI copolymers in several different selective solvents. The McConnell-Gast criterion was not found to be a determining factor, even though it could explain the fcc/bcc selection qualitatively. From the phase diagrams, the transition between fcc and bcc phases was also considered as a function of concentration and temperature, and their physical mechanisms are discussed based on the recent mean-field calculation reported by Grason.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.4
• /
• pp.1059-1066
• /
• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.37 no.8
• /
• pp.1015-1028
• /
• 2013

This study analyzed the relationship among swimming experiences, swimsuit selection criteria, swimsuit purchase satisfaction level, and the physical self-concept of female consumers. This study was based on a descriptive survey method using a questionnaire. The survey was conducted from June 15 through July 20, 2012, and the sample consisted of 330 female consumers in their 20s and 30s residing in the Seoul and Gyeonggi area. Factor analysis and Cronbach's ${\alpha}$ coefficients, ANOVA, Duncan's Test, and multiple regression analysis were employed for the data analysis. The results revealed that individual self-concepts on health, sports competence, and fitness were influenced by swimming experiences. There was a tendency for those with a longer period of swimming experience to have a higher level of brand consideration as a swimsuit selection criterion; in addition, they showed a higher satisfaction level with swimsuits. Individual physical self-concept influenced the consideration given to each swimsuit selection criterion as well as swimsuit purchase satisfaction level. The findings of the study reflect the possibility of utilizing swimming experiences as a criterion for swimsuit market segmentation. Attention to the quality of swimsuits as well as to the physical self-concept of consumers are required for marketing activities.

• Communications for Statistical Applications and Methods
• /
• v.13 no.3
• /
• pp.551-566
• /
• 2006

In this paper, we consider a Bayesian model selection for the intraclass correlation coefficient in familiar data. In particular, we compare two nested models such as the independence and intraclass models using the reference prior. A criterion for testing is the Bayesian Reference Criterion by Bernardo (1999) and the Intrinsic Bayes Factor by Berger and Pericchi (1996). We provide numerical examples using simulation data sets for illustration.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.643-646
• /
• 1987

A criterion is developed for the, selection of the best pairing of the control and manipulated variables and for the interaction analysis of decentralized multi-input multi-output control systems. This criterion is based on the difficulty caused by the interaction terms in finding the in-verse of the block steady gain matrix. A quantitative measure of the best pairing is obtained from the resemblance of a set of independent block multi-loop systems. Several examples show the validity of the pairing criterion.

• Journal of the Korean Statistical Society
• /
• v.20 no.2
• /
• pp.99-107
• /
• 1991

Behrens-Fisher problem is viewed from a model selection approach. Normal distribution is regarded as an approximating model, A criterion, called TIC, is derived and is compared with selection criteria such as AIC and a bootstrap estimator. Stochastic approximation is used since no closed form expression is available for the bootstrap estimator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.313-316
• /
• 2004

Model selection is the process that sets up the regularization parameter in the support vector machine or regularization network by using the external methods such as general cross validation or L-curve criterion. This paper suggests that the regularization parameter can be obtained simultaneously within the learning process of neural networks without resort to separate selection methods. In this paper, extended kernel method is introduced. The relationship between regularization parameter and the bias term in the extended kernel is established. Experimental results show the effectiveness of the new model selection method.

• The Korean Journal of Applied Statistics
• /
• v.30 no.1
• /
• pp.119-133
• /
• 2017

Corrected Akaike's information criterion (AICc) is known to have better finite sample properties. However, Akaike's information criterion (AIC) is still widely used to select an optimal prediction model among several candidate models due to of a lack of research on benefits obtained using AICc. In this paper, we compare the performance of AIC and AICc through numerical simulations and confirm the advantage of using AICc. In addition, we also consider the performance of quasi Akaike's information criterion (QAIC) and the corrected quasi Akaike's information criterion (QAICc) for binomial and Poisson data under overdispersion phenomenon.

• Journal of the Korean Statistical Society
• /
• v.23 no.2
• /
• pp.293-301
• /
• 1994

In this paper we propose a variable selection criterion minimizing influence curve in regression, and compare it with other criteria such as $C_p$(Mallows 1973) and adjusted coefficient of determination. Examples and extension to the generalized linear models are given.

• Journal of the Korean Statistical Society
• /
• v.25 no.2
• /
• pp.195-203
• /
• 1996

A bootstrap method is used to correct the apparent downward bias of a naive plug-in bootstrap model selection criterion, which is shown to enjoy a high degree of accuracy. Comparison of bootstrap method with the asymptotic method is made through an illustrative example.