• The Korean Journal of Applied Statistics
• /
• v.26 no.2
• /
• pp.267-280
• /
• 2013

College tuition is a significant economic, social, and political issue in Korea. We conduct a Bayesian analysis of a hierarchical model to address the factors related to college tuition based on a survey data collected by Statistics Korea. A binary response variable is selected depending on if more than 70% of tuition costs are supported by parents, and a hierarchical Probit model is constructed with areas as groups. A set of explanatory variables is selected from a factor analysis of available variables in the survey. A Markov chain Monte Carlo algorithm is used to estimate parameters. From the analysis results, income and stress are significantly related to college tuition support from parents. Parents with high income tend to support children's college tuition and students with parents' financial support tend to be mentally less stressed; subsequently, this shows that the economic status of parents significantly affects the mental health of college students. Gender, a healthy life style, and college satisfaction are not significant factors. Comparing areas in terms of the degrees of correlation between stress/income and tuition support from parents, students in Kangwon-do are the most mentally stressed when parents' support is limited; in addition, the positive correlation between parents support and income is stronger in big cities compared to provincial areas.

• The Korean Journal of Applied Statistics
• /
• v.27 no.6
• /
• pp.909-922
• /
• 2014

This paper reviews Bayesian inference with inequality constraints. It focuses on ⅰ) comparison of models with various inequality/equality constraints on parameters, ⅱ) multiple tests on equalities of parameters when parameters are under inequality constraints, ⅲ) multiple test on equalities of score parameters in models for contingency tables with ordinal categorical variables.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.660-667
• /
• 2007

본 논문은 확률특성을 갖는 네트워크 기반 제어시스템(NCS; Networked Control Systems)을 위하여 동적 베이시안 네트워크(DBN; Dynamic Bayesian Networks)와 신경회로망 기법을 이용한 지능제어기법을 제안한다. 신경회로망은 시변 시간지연을 갖는 비선형 시스템의 실시간 오차를 보상하기 위한 제어기의 최적화에 적용된다. 모듈화 신경회로망이 구성되며 이것은 제어기의 파라미터를 출력한다 가장 간단한 DBN 구조인 마코브 체인(MC; Markov Chain)이 구성되며 NCS의 랜덤 관측값을 모델링에 적용되며 예측 제어기의 구성에 또한 사용된다. 제안한 제어기법은 위성시스템의 자세제어에 적용하여 컴퓨터 시뮬레이션을 통해 성능을 검증하였다.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.51.3-52
• /
• 2020

We present the status of the development of a Markov Chain Monte Carlo (MCMC) parameter estimation (PE) pipeline for compact binary coalescences (CBCs) with the Japanese KAGRA gravitational-wave (GW) detector. The pipeline is included in the KAGRA Algorithm Library (KAGALI). Basic functionalities are benchmarked from the LIGO Algorithm Library (LALSuite) but the KAGRA MCMC PE pipeline will provide a simpler, memory-efficient pipeline to estimate physical parameters from gravitational waves emitted from compact binaries consisting of black holes or neutron stars. Applying inspiral-merge-ringdown and inspiral waveforms, we performed simulations of various black hole binaries, we performed the code sanity check and performance test. In this talk, we present the situation of GW observation with the Covid-19 pandemic. In addition to preliminary PE results with the KAGALI MCMC PE pipeline, we discuss how we can optimize a CBC PE pipeline toward the next observation run.

• The Korean Journal of Applied Statistics
• /
• v.15 no.1
• /
• pp.73-84
• /
• 2002

Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.

• The Korean Journal of Applied Statistics
• /
• v.11 no.2
• /
• pp.377-387
• /
• 1998

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced latent variables that indicates with component of the Superposition model. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Metropolis algorithms along with Gibbs steps are proposed to preform the Bayesian inference of such models. for model determination, we explored the Pre-quential conditional predictive Ordinate(PCPO) criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions, we consider in this paper Superposition of Musa-Okumoto and Erlang(2) models. A numerical example with simulated dataset is given.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.174-174
• /
• 2018

현재 우리나라에서는 지속적으로 홍수 및 가뭄에 대한 예방사업을 진행하고 있음에도 불구하고 해마다 피해가 발생하고 있으며, 이에 따라 효율적인 이수 치수 계획이 절실히 필요한 실정이다. 하지만 우리나라의 경우, 강우의 발생 특성이 과거와는 다른 양상을 보이고 있다. 따라서 강우빈도해석 시 강우특성이 변화하지 않는다는 정상성(stationary)을 가정하는 기존의 방법론은 문제가 있다. 이에 본 연구에서는 강우특성이 어떻게 변화하였는지 평가하는 방법론에 대하여 고찰하고자 한다. 우선, 대상 강우관측소의 과거 일강수량 자료를 수집하고 연도별 강우발생 천이확률(Markov Chain)과 강우량 확률분포(Gamma)의 매개변수를 산정한다. 그리고 일강우시계열의 경향성 분석(Mann-Kendall test) 결과와 함께 비교 검토하여 어떠한 방법론이 강우특성 변화를 더욱 잘 추정하는지에 대하여 평가한다. 본 연구를 통하여 우리나라 강우특성의 변화를 더욱 정확하게 추정할 수 있는 기틀을 마련할 수 있을 것이며, 향후 비정상성 기반의 기후변화 모의를 수행하기 위한 기초연구로 활용될 수 있을 것으로 기대된다.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.7
• /
• pp.1-8
• /
• 2009

In this paper, make a study decision problem called an optimal release policies after testing a software system in development phase and transfer it to the user. The optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement is generally accepted. The Bayesian parametric inference of model using log Poisson execution time employ tool of Markov chain(Gibbs sampling and Metropolis algorithm). In a numerical example by T1 data was illustrated. make out estimating software optimal release time from the maximum likelihood estimation and Bayesian parametric estimation.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.28 no.3
• /
• pp.229-239
• /
• 2015

In light of the significant increase in the quantities of goods transported and the development of the shipping industry, the frequency of usage of port structures has increased; yet, the government's budget for the shipping & port of SOC has been reduced. Port structures require systematically effective maintenance and management trends that address their growing frequency of usage. In order to construct a productive maintenance system, it is essential to develop deterioration models of port structures that consider various characteristics, such as location, type, use, constructed level, and state of maintenance. Processes for developing such deterioration models include examining factors that cause the structures to deteriorate, collecting data on deteriorating structures, and deciding methods of estimation. The techniques used for developing the deterioration models are multiple regression analysis and Markov chain theory. Multiple regression analysis can reflect changes over time and Markov chain theory can apply status changes based on a probabilistic method. Along with these processes, the deterioration models of open-type and gravity-type wharfs were suggested.

• The Korean Journal of Applied Statistics
• /
• v.30 no.6
• /
• pp.917-929
• /
• 2017

Korean income data obtained from Korea Labor Panel Survey shows excessive zeros, which may not be properly explained by the Tobit model. In this paper, we analyze the data using a zero-inflated Tobit model to incorporate excessive zeros. A zero-inflated Tobit model consists of two stages. In the first stage, individuals with 0 income are divided into two groups: genuine zero group and random zero group. Individuals in the genuine zero group did not participate labor market since they have no intention to do so. Individuals in the random zero group participated labor market but their incomes are very low and truncated at 0. In the second stage, the Tobit model is assumed to a subset of data combining random zeros and positive observations. Regression models are employed in both stages to obtain the effect of explanatory variables on the participation of labor market and the income amount. Markov chain Monte Carlo methods are applied for the Bayesian analysis of the data. The proposed zero-inflated Tobit model outperforms the Tobit model in model fit and prediction of zero frequency. The analysis results show strong evidence that the probability of participating in the labor market increases with age, decreases with education, and women tend to have stronger intentions on participating in the labor market than men. There also exists moderate evidence that the probability of participating in the labor market decreases with socio-economic status and reserved wage. However, the amount of monthly wage increases with age and education, and it is larger for married than unmarried and for men than women.