• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.153-163
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• 2024

Since the onset of the COVID-19 pandemic, there has been a significant shift in the lifestyle patterns of the populace across various domains. Concerns surrounding COVID-19 have emerged as pivotal catalysts of change in recreational habits with people giving a particular preference for environments with low population density and increased openness. This trend has resulted in an uptick in excursions to natural reserves, coastlines, and parks. However, during the peak of infectious outbreaks, widespread adherence to social distancing measures has precipitated a steep decline in tourist footfall across natural recreation forests, exacerbating financial deficits to a considerable extent. Thus, this research sought to compare and analyze the operational efficacy and productivity of national, public, and private natural recreation forests pre- and post-COVID-19 pandemic by utilizing non-parametric methodologies, such as data envelopment analysis and the Malmquist productivity index analysis. The objective was to identify the factors contributing to the decreases in efficiency and productivity and ultimately offer nuanced recommendations tailored to respective administrative bodies. This study's distinctive focus on the analysis of management efficiency and productivity in natural recreation forests nationwide offers significant academic and practical relevance.

• Journal of Korean Institute of Industrial Engineers
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• v.6 no.2
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• pp.37-47
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• 1980

A system of conditional sampling plans composed of three stages is developed. In the first stage, the decision to accept or reject the lot is based on the information obtained from the current lot. When a decision is not made in the first stage, a second stage is introduced and the information from the immediately preceding lot as well as the information from the current lot is used for the decision. When a decision is not made in the first stage, a second stage is introduced and the information from the immediately preceding lot as well as the information from the current lot is used for the decision. When a decision is not made in the second stage either, the decision is deferred until the information from the immediately following lot is obtained. Existing tables for constructing double sampling plans with $_2$=$2n_1$ can be used to find the parameters of these plans. These sampling plans can bring sizable savings in the amount of inspection when the process is relatively stable. The response delay to the change in process quality and the deferred events may be considered as shortcomings of these plans. However, these are not serious in practical applications, and the reduction in sample size may more than offset these shortcomings.

• Environmental and Resource Economics Review
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• v.21 no.4
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• pp.921-945
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• 2012

Conditional Logit (CL) model is widely used since its model estimation and interpretation of results of the model is relatively easy, on the other hand, it has the limit of preference heterogeneity of respondents being not fully considered. In this study we used the two models, Mixed Logit (ML) Model and Latent Class Model (LCM) to explain preference heterogeneity of respondents for protection for Boryeong Dam wetland. As a result of the examination for heterogeneity in Boryeong city and six metropolitan areas, we found there was significant difference between two regions. While there was explicit preference heterogeneity within respondents in Boryeong city, we found little heterogeneity within respondents in six metropolitan areas. Thus in the case of six metropolitan areas, CL model can be used for parameter estimation while in the case of Boryeong city, WTP estimates are based on parameter estimates from ML model to reflect the heterogeneity within respondents. Additionally, ML model with interaction and 2-class LCM for respondents in Boryeong city were used to explain the sources of the heterogeneity. The ML model with interaction has advantage of explaining individual unobserved heterogeneity. However The comarison between these two models reflects the fact that LCM provided added information that was not conveyed in the ML model with interaction. Thus, Preference heterogeneity within respondents in this study may be better explained by class level through LCM rather than indiviual level through ML model.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.517-541
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• 2019

According to the OECD (2015) and UN (2017), Korea was classified as an immigration country. The designation as an immigration country means that net migration will remain positive and international migration is likely to affect population growth. KOSTAT (2011) used a model with more than 15 parameters to divide sexes, immigration and emigration based on the Wilson (2010) model, which takes into account population migration factors. Five years later, we assume the average of domestic net migration rate for the last five years and foreign government policy likely quota. However, both of these results were conservative estimates of international migration and provide different results than those used by the OECD and UN to classify an immigration country. In this paper, we proposed a stochastic projection on international migration using nonparametric model (FDM by Hyndman and Ullah (2007) and Coherent FDM by Hyndman et al. (2013)) that uses a functional data model for the international migration data of Korea from 2000-2017, noting the international migration such as immigration, emigration and net migration is non-linear and not linear. According to the result, immigration rate will be 1.098(male), 1.026(female) in 2018 and 1.228(male), 1.152(female) in 2025 per 1000 population, and the emigration rate will be 0.907(male), 0.879(female) in 2018 and 0.987(male), 0.959(female) in 2025 per 1000 population. Thus the net migration is expected to increase to 0.191(male), 0.148(female) in 2018 and 0.241(male), 0.192(female) in 2025 per 1000 population.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.

• The Journal of Korean Association of Computer Education
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• v.23 no.1
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• pp.53-61
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• 2020

In the information society, the ability of learners to use computers to conduct self-directed learning is important. Indeed, the higher the computer's ability to use computers, the more the academic achievement needs to be analyzed. The purpose of this study was to identify longitudinal trajectories of student awareness of ICT literacy and frequency of computer use. We also examined the effects of the longitudinal patterns on academic achievement and college major choice. A non-parametric approach, K-means for longitudinal data(KML) algorithm, was conducted using 9-year longitudinal data from Seoul Education Longitudinal Study (2010-2018). Findings indicated that a pattern presenting a higher awareness of ICT literacy and frequency of computer use showed better academic achievements and was likely to prefer to choose engineering-related majors.

• Journal of Gifted/Talented Education
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• v.17 no.1
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• pp.1-26
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• 2007

The purpose of this study was to develop a math test for identification of the mathematically gifted on the basis of their math creative problem solving ability and to evaluate the goodness of the test. Especially, testing reliability and validity of scoring method on the basis of fluency only for evaluation of math creative problem solving ability was one of the main purposes. Ten closed math problems and 5 open math problems were developed requiring math thinking abilities such as intuitive insight, organization of information, inductive and deductive reasoning, generalization and application, and reflective thinking. The 10 closed math test items of Type I and the 5 open math test items of Type II were administered to 1,032 Grade 7 students who were recommended by their teachers as candidates for gifted education programs. Students' responses were scored by math teachers. Their responses were analyzed by BIGSTEPS and 1 parameter model of item analyses technique. The item analyses revealed that the problems were good in reliability, validity, item difficulty and item discriminating power even when creativity was scored based on the single criteria of fluency. This also confirmed that the open problems which are less-defined, less-structured and non-entrenched were good in measuring math creative problem solving ability of the candidates for math gifted education programs. In addition, it was found that the math creative problem solving tests discriminated applicants for the two different gifted educational institutions.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.725-731
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• 2008

In this paper, we propose a novel construction scheme for the personalized e-Learning system based on IRT(item response theory), which can be applied to the content including non-hierarchical and horizontal learning objects in its learning nodes. Especially the proposed system performs tests and re-estimates examinee ability during the learning nodes are operating so that the results are directly applied to the next node. This scheme can be called a dynamic relationship between test and learning which is totally different from conventional customization based on learning procedures separated from test steps. Moreover, we should periodically modify the averages of node difficulties, item parameters, and ability parameters of students so that the system have more accurate personalized learning capability. As a result, this scheme maximizes learning efficiency offering the most appropriate learning objects and items to the individual students according to their estimated abilities and the system itself should obtain continuous improvements by modifying the parameters and fulfilling periodical feedbacks.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.