• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.103-115
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• 2007

In this paper we studied the small sample asymptotic inference for the autoregressive coefficient in AR(1) model. Based on saddlepoint approximations to the distribution of quadratic forms, we suggest a new approximation to the distribution of the estimators of the noncircular autoregressive coefficients. Simulation results show that the suggested methods are very accurate even in the small sample sizes and extreme tail area.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.381-397
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• 2004

The marginal likelihood has become an important tool for model selection in Bayesian analysis because it can be used to rank the models. We discuss the marginal likelihood for Poisson regression models that are potentially useful in small area estimation. Computation in these models is intensive and it requires an implementation of Markov chain Monte Carlo (MCMC) methods. Using importance sampling and multivariate density estimation, we demonstrate a computation of the marginal likelihood through an output analysis from an MCMC sampler.

• Fire Science and Engineering
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• v.20 no.2 s.62
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• pp.54-63
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• 2006

The number of forest fire under various conditions such as year, month, time, day of the week, region, damaged species, cause, and damaged area are checked, and the statistics of the forest fire causing materials in recent 14 years ('91-'04) are analyzed. The result shows that the year majority of forest fires had happened in last 14 year was 2001 and most of forest fire occurred in April, Sunday, around 14:00 to 15:00. The most damaged region is Gyeongsangbuk-Do, followed by Gangwon-Do, Jeollabuk-Do, and Gyeonggi-Do. The most damaged species is pine tree. The main causes of forest fires are accidental fire and incineration of a field boundary; however, recently, incendiarism is increased. The result of analysis on the damaged area shows that small fires under 5 ha occurred most frequently and large fires (over 30 ha) occurred mostly in Kangwon province (44.2%). The result also shows that the large forest fires (1,113 minutes) require 7.5 time more than the small forest fires (148 minutes). Especially, since average damaged area caused by large forest fire was about 470 ha per incident.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1169-1182
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• 2016

Cancer is a typical cause of death in Korea that becomes a major issue in health care. According to Cause of Death Statistics (2014) by National Statistical Office, SMRs (standardized mortality rates) in Busan were counted as the highest among all cities. In this paper, we used data of Busan Regional Cancer Center to estimate the extent of the cancer incidence rate and cancer mortality rate. The data are considered in small areas of administrative units such as Gu/Dong from years 2003 to 2009. All cancer including four major cancers (stomach cancer, colorectal cancer, lung cancer, liver cancer) have been analyzed. We carried out model selection and parameter estimation using spatial multi-level model incorporating a spatial correlation. For the spatial effects, CAR (conditional autoregressive model) has been assumed.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.281-288
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• 2022

According to statistics conducted by the Korea Coast Guard, the number of accidents on small boats under 5 tons is increasing every year. This is because only a small number of people are on board. The previously developed maritime distress and safety systems are not well distributed because passengers must be equipped with additional remote equipment. The purpose of this study is to develop a distress awareness system that recognizes man over-board situations in real time. This study aims to present the part of the passenger tracking system among the small ship's distress awareness situational system that can generate passenger's location information in real time using deep learning based object detection and tracking technologies. The system consisted of the following steps. 1) the passenger location information is generated in the form of Bounding box using its detection model (YOLOv3). 2) Based on the Bounding box data, Deep SORT predicts the Bounding box's position in the next frame of the image with Kalman filter. 3) When the actual Bounding Box is created within the range predicted by Kalman-filter, Deep SORT repeats the process of recognizing it as the same object. 4) If the Bounding box deviates the ship's area or an error occurs in the number of tracking occupant, the system is decided the distress situation and issues an alert. This study is expected to complement the problems of existing technologies and ensure the safety of individuals aboard small boats.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.709-720
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• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1547-1555
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• 2017

This paper studies Bayesian test of homogeneity in contingency tables made by discretizing a continuous variable. Sometimes when we are considering events of interest in small area setup, we can think of discretization approaches about the continuous variable. If we properly discretize the continuous variable, we can find invisible relationships between areas (groups) and a continuous variable of interest. The proper discretization of the continuous variable can support the alternative hypothesis of the homogeneity test in contingency tables even if the null hypothesis was not rejected through k-sample tests involving one-way ANOVA. In other words, the proportions of variables with a particular level can vary from group to group by the discretization. If we discretize the the continuous variable, it can be treated as an analysis of the contingency table. In this case, the chi-squared test is the most commonly employed method. However, further discretization gives rise to more cells in the table. As a result, the count in the cells becomes smaller and the accuracy of the test becomes lower. To prevent this, we can consider the Bayesian approach and apply it to the setup of the homogeneity test.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1001-1012
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• 2016

It is well known that air pollutants exert a bad influence on human health. According to the United Nations Environment Program, 4.3 million people die from carbon monoxide and particulate matter annually from all over the world. Carbon monoxide is a toxic gas that is the most dangerous of the gas consisting of carbon and oxygen. In this paper, we used 1 hour, 6 hours, 12 hours, and 24 hours average carbon monoxide concentration data collected between 2004 and 2013 in Daegu Gyeongbuk area. Parameters of the generalized extreme value distribution were estimated by maximum likelihood estimation and L-moments estimation. An evalution of goodness of fitness also was performed. Since the number of samples were small, L-moment estimation turned out to be suitable for parameter estimation. We also calculated 5 year, 10 year, 20 year, and 40 year return level.

• Journal of the Korea Safety Management & Science
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• v.19 no.4
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• pp.95-104
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• 2017

Among the industrial disasters caused by drop, ladder related drop disasters are occurred the mostly. The victims are occurred continuously. This study analyzed current situation of industrial disasters for recent 10 years(2005~2014) and compared analyzed statistics of death disasters of ladder by workplace scale, age, occupation, employment type, working content, scarred area, etc. in detail. This study suggested direction of safety standard modification for ladder that is an original cause material of many drops, direction of safety training strengthening of small-scaled workplace, and safety model based on disaster statistics and should contribute to reduction of disaster rate for ladder working.

• Journal of Korean Society of Forest Science
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• v.99 no.3
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• pp.285-291
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• 2010

Since 2006, the $5^{th}$ National Forest Inventory (NFI) has been implemented to provide forest resources statistics at the national level and at the county level as well. However, it needs a small-area estimator for estimating forest statistics at the county-level due to a small number of samples collected within a county. This study was conducted to evaluate the applicability of a geographical-based synthetic estimator for estimating forest growing stock volumes at the county level. The NFI-field plots surveyed were post-stratified into three forest cover types. In the synthetic estimator, field plots within a geographical-based super-county for each county were used to estimate stratum weights and stratum mean volumes. It was resulted that estimated stratum weights using the synthetic estimation were significantly differ from forest cover maps. The standard errors of estimated mean by the synthetic estimation that ranged from ${\pm}3.5\;m^3$/ha to ${\pm}7.7\;m^3$/ha were more smaller than those (${\pm}7.8\;m^3/ha{\sim}{\pm}24.7\;m^3/ha$) by the direct estimation. This means that the synthetic estimation is possible to provide more precise estimates of mean volumes.