• Journal of the Korean Data and Information Science Society
• /
• v.25 no.4
• /
• pp.715-725
• /
• 2014

This study uses a quantile regression analysis to investigate intergenerational economic mobility in Korea. The analysis is based on data from the 1st through 11th waves of the Korean Labor and Income Panel Study (KLIPS) conducted from 1998-2008. The household nature of the data allows us to link parents' incomes to children's incomes at different points in time. Using a quantile regression analysis instead of mean one reveals that the effect of fathers' earnings are different across the conditional distribution of sons' earnings, particularly being larger on the upper quantile than on the lower quantile. After controlling effect of sons' college education by including a dummy variable for the degree, however, the pattern among quantile effects for fathers' earnings is no longer clear. Instead a new pattern emerges that education has a much larger effect on the upper quantiles than on the lower ones. Using nonparametric estimates of conditional density curves based on the quantile regression results, we derive some interesting features in graphical forms, which are not obvious in numerical analysis.

• Journal of Korean Society of Forest Science
• /
• v.99 no.6
• /
• pp.891-899
• /
• 2010

The purpose of this study is to estimate economic value of forest landscape function using conditional logit model, applied by Choice Experiment. For the study, we have chosen attributes and levels of forest landscape. In specific, topographical forest type, forest type, forest density, recreational factor (side trip, accessibility of valley) and WTP were included in attributes. Based on factors, we have made 48 choice sets with Balanced and Orthogonal form using SAS 9.1. The efficiency of questionnaire was 6.02 (D-Error: 0.1) and choice set and socio-economic variable were selected. In order to reduce cognitive load of respondent, 96 choice sets were divided into 4 types in questionnaire so that respondent could respond to 12 choice sets respectively. Population was citizens from 7 metropolitan cities including Seoul, and the interview survey was conducted to find out average annual WTP per household for the total 280 interviewees. As a result, In the Non-ASC model, Mcfadden' ${\rho}$ had 0.21, and Log Likelihood: -2,631. Average annual WTP per household for forest landscape was 266,723 Won(Korean currency).

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.4
• /
• pp.512-517
• /
• 2008

It is show that increasing of aged and handicapped people requires development of Ubiquitous computing technique to offer the specialized service for handicapped-people. For this, we need a development of Context Awareness and Service Reasoning Technique that the technique is supplied interaction between user and U-environment instead of the old unilateral relation. The old research of context awareness needed probabilistic presentation model like a Bayesian Network based on expert Systems for recognize given circumstance by a domain of uncertain real world. In this article, we define a domain of disorder activity assistant service application and context model based on ontology in diversified environment and minimized intervention of user and developer. By use this context model, we apply the structure learning of Bayesian Network and decide the service and activity to development of application service for handicapped people. Finally, we define the proper Conditional Probability Table of the structured Bayesian Network and if random situation is given to user, then present state variable of Activity and Service by given Causal relation of Bayesian Network based on Conditional Probability Table and it can be result of context awareness.

• Journal of the Korean Society of Combustion
• /
• v.18 no.1
• /
• pp.13-20
• /
• 2013

Leading edge statistics are obtained by direct numerical simulation(DNS) of freely propagating incompressible and stagnating compressible turbulent premixed flames. Conditional averages of velocities in terms of reaction progress variable, c, and local flame surface density, ${\sum}^{\prime}_f$, are defined and compared through the flame brush. It holds asymptotically that $<u>_f=<S_d>_f$ and $<u>_u-<u>_b=D_t/L_w$ with the characteristic length scale of $\bar{c}$ variation, $L_w$. It also holds that $<u>_b=<u>_f$ for a freely propagating flame under no mean strain rate. The turbulent burning velocity, $S_T$, is determined by the conditional statistics at the leading edge under large activation energy.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.21 no.3
• /
• pp.427-435
• /
• 1997

Conditional sampling techniques are utilized to investigate the relation between the wall skin-friction and stream wise velocity fluctuations in a turbulent boundary layer. Conditionally averaged results using a peak detection and the VITA (variable-interval time-averaging) technique show that a high skin friction is associated with high frequency components of the wall skin-friction fluctuations. The conditionally averaged wall skin-friction fluctuations obtained by using the VITA technique have a positively-skewed characteristics compared with the conditionally averaged stream wise velocity fluctuations. It is confirmed that there exists a phase shift between the wall skin-friction and stream wise velocity fluctuations, which was also found from the long-time averaged space-time correlations. The amount of phase shift between the wall skin-friction and stream wise velocity fluctuations is the same as that from the long-time averaged space-time correlations and does not change despite the variation of the detection threshold.

• The Korean Journal of Applied Statistics
• /
• v.27 no.6
• /
• pp.975-989
• /
• 2014

Lasso (Tibshirani, 1996) and Elastic Net (Zou and Hastie, 2005) have been widely used in various fields for simultaneous variable selection and coefficient estimation. Bayesian methods using a conditional Laplace and a double Pareto prior specification have been discussed in the form of hierarchical specification. Full conditional posterior distributions with each priors have been derived. We compare the performance of Bayesian lassos with Laplace prior and the performance with double Pareto prior using simulations. We also apply the proposed Bayesian hierarchical models to real data sets to predict the collapse of governments in Asia.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.10
• /
• pp.165-176
• /
• 2021

The primary purpose of this study is to identify determinants influencing Vietnamese consumers' purchase intention towards Japanese convenience goods. The research model consisting of six factors affecting the dependent variable is proposed based on the Theory of Consumption Values and Theory of Perceived Value of purchase. This study employs a survey method in a convenient sampling method to collect data of target respondents. Data consisting of 180 samples was collected and analyzed using the SMARPLS3 software. The measurement model is assessed to confirm the validity and reliability of the construct, then hypotheses testing is performed with Bootstrapping analysis. The results demonstrated that five factors affect Vietnamese consumers' purchase intention towards Japanese convenience goods, including the price of the product, quality of the product, functional value, emotional value, and conditional value. Major findings of this study suggest that the functional value that consumer perceives about Japanese convenience goods has the most significant relationship with their purchase intention, followed by the quality of the product and the price of the product. Besides, emotional value and conditional value have a moderate influence on consumers' purchase intention. Whereas the influence of the epistemic value of the product on consumers' purchase intention is insignificant in this presented study.

• Journal of the Korean Geotechnical Society
• /
• v.23 no.3
• /
• pp.51-59
• /
• 2007

A reliability-based approach that can systematically model various sources of uncertainty is presented in the context of slope stability. Expressions for characterization of soil properties are developed in order to incorporate sampling errors, spatial variability and its effect of spatial averaging. Reliability analyses of slope stability with different statistical representations of soil properties show that the incorporation of sampling error, spatial correlation, and conditional simulation leads to significantly lower probability of failure than that obtained by using simple random variable approach. The results strongly suggest that the spatial variability and sampling error have to be properly incorporated in slope stability analysis.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1361-1371
• /
• 2016

Quantile regression provides a variety of useful statistical information to examine how covariates influence the conditional quantile functions of a response variable. However, traditional quantile regression (which assume a linear model) is not appropriate when the relationship between the response and the covariates is a nonlinear. It is also necessary to conduct variable selection for high dimensional data or strongly correlated covariates. In this paper, we propose a penalized quantile regression tree model. The split rule of the proposed method is based on residual analysis, which has a negligible bias to select a split variable and reasonable computational cost. A simulation study and real data analysis are presented to demonstrate the satisfactory performance and usefulness of the proposed method.

• The Korean Journal of Applied Statistics
• /
• v.29 no.6
• /
• pp.1095-1106
• /
• 2016

The quantile regression method proposed by Koenker et al. (1978) focuses on conditional quantiles given by independent variables, and analyzes the relationship between response variable and independent variables at the given quantile. Considering the linear programming used for the estimation of quantile regression coefficients, the model fitting job might be difficult when large data are introduced for analysis. Therefore, dimension reduction (or variable selection) could be a good solution for the quantile regression of large data sets. Regression tree methods are applied to a variable selection for quantile regression in this paper. Real data of Korea Baseball Organization (KBO) players are analyzed following the variable selection approach based on the regression tree. Analysis result shows that a few important variables are selected, which are also meaningful for the given quantiles of salary data of the baseball players.