• Analytical Science and Technology
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• v.27 no.1
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• pp.41-59
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• 2014

The aims of this work were the identification and the classification of fresh lubricants and used engine oils of vehicles for the application in forensic science field-80 kinds of fresh lubricants were purchased and 86 kinds of used engine oils were sampled from 24 kinds of diesel and gasoline vehicles with different driving conditions. The sample of lubricants and used engine oils were analyzed by GC/MS. The Bayesian model technique was developed for classification or identification. Both the wavelet fitting and the principal component analysis (PCA) techniques as a data dimension reduction were applied. In fresh lubricants classification, the rates of matching by Bayesian model technique with wavelet fitting and PCA were 97.5% and 96.7%, respectively. The Bayesian model technique with wavelet fitting was better to classify lubricants than it with PCA based on dimension reduction. And we selected the Bayesian model technique with wavelet fitting for classification of lubricants. The other experiment was the analysis of used engine oils which were collected from vehicles with the several mileage up to 5,000 km after replacing engine oil. The eighty six kinds of used engine oil sample with the mileage were collected. In vehicle classification (total 24 classes), the rate of matching by Bayesian model with wavelet fitting was 86.4%. However, in the vehicle's fuel type classification (whether it is gasoline vehicle or diesel vehicle, only total 2 classes), the rate of matching was 99.6%. In the used engine oil brands classification (total 6 classes), the rate of matching was 97.3%.

• Asia Marketing Journal
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• v.11 no.1
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• pp.73-92
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• 2009

Consumers influence other consumers' brand choice behavior by delivering a variety of objective or subjective information on a particular product, which is called WOM (Word-Of-Mouth) activities. For WOM activities, WOM senders should choose messages to deliver to other consumers. We classify the contents of the messages a consumer chooses for WOM delivery into two categories: Subjective (positive or negative) evaluation and objective information on products. In our study, we regard WOM senders' activities as a choice behavior and introduce a choice model to study the relationship between the choice of different WOM information (WOM with positive or negative subjective evaluation and WOM with objective information) and its influencing factors (information sources and consumer characteristics) by developing two bivariate Probit models. In order to consider the mediating effects of WOM senders' product involvement, product attitude, and their characteristics (gender and age), we develop three second-level models for the propagation of positive evaluations, of negative evaluations, and of objective information on products in an hierarchical Bayesian modeling framework. Our empirical results show that WOM senders' information choice behavior differs according to the types of information sources. The effects of information sources on WOM activities differ according to the types of WOM messages (subjective evaluation (positive or negative) and objective information). Therefore, our study concludes that WOM activities can be partially managed with effective communication plans influencing on consumers' WOM message choice behavior. The empirical results provide some guidelines for consumers' propagation of information on products companies want.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• 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.

• Journal of Korean Society of Transportation
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• v.30 no.6
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• pp.37-46
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• 2012

The number of traffic accidents caused by elderly drivers over the age of 65 has surged over the past ten years from 37,000 to 274,000 cases. The proportion of elderly drivers' accidents has jumped 3.1 times from 1.2% to 3.7% out of all traffic accidents, and traffic safety organizations are pursuing diverse measures to address the situation. Above all, connecting safety measures with an in-depth research on behavioral and physical characteristics of elderly drivers will prove vital. This study conducted an empirical research linking the driving characteristics and traffic accidents by elderly drivers based on the Driving Aptitude Test items and traffic accident data, which enabled the measurement of behavioral characteristics of elderly drivers. In developing the Influence Model, we applied the zero-inflated Poisson (ZIP) regression model and selected an accident prediction model based on the Bayesian Influence in regards to the ZIP regression model and the zero-inflated negative binomial (ZINB) regression model. According to the results of the AAE analysis, the ZIP regression model was more appropriate and it was found that three variables? prediction of velocity, diversion, and cognitive ability? had a relation of influence with traffic accidents caused by elderly drivers.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.533-541
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• 2005

We identify the correct chromosome and locate the corresponding markers close to the QTL in the linkage analysis of a quantitative trait by using the SSVS method. We consider several markers linked to the QTL, as well as to each oyher and thus the i.b.d. values at these loci generate collinear predictors to be evaluated when using the SSVS approach. The results on considering only closely linked markers to two QTL simultaneously showed clear evidence in favor of the closest marker to the QTL considered over other markers. The results of the analysis of collinear markers with SSVS showeed high concordance to those obtained using traditional multiple regression. We conclude based on this simulation study that the SSVS is quite useful to identify linkage with multiple linked markers simultaneously for a complex quantitative trait.

• Journal of Energy Engineering
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• v.27 no.4
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• pp.36-49
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• 2018

This study analyzed the public preference and acceptance regarding renewable energy projects through Choice Based Conjoint Analysis. The results show that the surveyed respondents consider the leading authority of the projects, as the most important factor when considering participating in renewable energy initiatives. Following this, the mode of participation and profit distribution and the power plant location are also viewed as important, whereas participation through decision making regarding the projects was less important. Also when participating in renewable energy projects, respondents tend to prefer to financially participating through loans or owning shares rather than volunteering support for the business such as sharing information, stating one's views, or providing cooperation and coordination. Therefore, the focus is on distributional justice, such as financial investment and profit distribution, rather than procedural justice, for instance decision making. When analyzing the part-worths utilities for the participation attribute, the respondents most preferred to receiving dividends based on earnings by owning shares with the local government in charge of the entire projects. As a consequence, the results suggest that it is important to have local government get involved and have trust-worthy governing systems in place for the initiation of the public participating-renewable energy projects.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.581-594
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• 2011

The traditional method of 3+3 standard design and model-based Bayesian continual reassessment method (CRM) are commonly used in Phase I clinical trials to identify the maximal tolerated dose(MTD) of a new drug. In this paper we review clinical examples of Phase I trials that were carried out in patients with refractory or relapsed leukemia and myelodysplastic syndrome. The recently proposed 3+1+1 design and rolling-6 design can shorten the trial duration, when a very slow accrual of patients with a simple 3+3 standard design may result in the untimely termination of trials. Too conservative approaches in determining the dose levels in Phase I clinical trials can leave clinical investigators unable to accurately determine the MTD. When determining future patient doses, the designs that use a time-to-event CRM can cooperate late toxicities by accounting for the proportion of the observation period of each enrolled patient. With the CRM design, simulations under different scenarios during the trial are important in detecting the under- or over-estimation of the initial estimate of the dose-limiting toxicity rate for each dose level. We present the advantages and drawbacks of the designs used in Phase I clinical trials for leukemia patients.