Jun-Tae Han

Estimation for the Half Logistic Distribution under Progressive Type-II Censoring

In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a half-logistic distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples. We also obtain the approximate maximum likelihood estimators of the reliability function using the proposed estimators. We compare the proposed estimators in the sense of the mean squared error.

Estimation for the Half Logistic Distribution Based on Double Hybrid Censored Samples

Many articles have considered a hybrid censoring scheme, which is a mixture of Type-I and Type-II censoring schemes. We introduce a double hybrid censoring scheme and derive some approximate maximum likelihood estimators(AMLEs) of the scale parameter for the half logistic distribution under the proposed double hybrid censored samples. The scale parameter is estimated by approximate maximum likelihood estimation method using two different Taylor series expansion types. We also obtain the maximum likelihood estimator(MLE) and the least square estimator(LSE) of the scale parameter under the proposed double hybrid censored samples. We compare the proposed estimators in the sense of the mean squared error. The simulation procedure is repeated 10,000 times for the sample size n = 20(10)40 and various censored samples. The performances of the AMLEs and MLE are very similar in all aspects but the MLE and LSE have not a closed-form expression, some numerical method must be employed.

Estimation for the Double Rayleigh Distribution Based on Multiply Type-II Censored Samples

In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the location parameter in a double Rayleigh distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

An Estimation of the Entropy for a Double Exponential Distribution Based on Multiply Type-II Censored Samples

In many life-testing and reliability studies, the experimenter might not always obtain complete information on failure times for all experimental units. Multiply Type-II censored sampling arises in a life-testing experiment whenever the experimenter does not observe the failure times of some units placed on a life-test. In this paper, we obtain estimators for the entropy function of a double exponential distribution under multiply Type-II censored samples using the maximum likelihood estimation and the approximate maximum likelihood estimation procedures. We compare the proposed estimators in the sense of the mean squared errors by using Monte Carlo simulation.

Goodness-of-fit Test for the Weibull Distribution Based on Multiply Type-II Censored Samples

In this paper, we derive the approximate maximum likelihood estimators of the shape parameter and the scale parameter in a Weibull distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We develop three modified empirical distribution function type tests for the Weibull distribution based on multiply Type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

A Development of a Tailored Follow up Management Model Using the Data Mining Technique on Hypertension

This study used the characteristics of the knowledge discovery and data mining algorithms to develop tailored hypertension follow up management model - hypertension care predictive model and hypertension care compliance segmentation model - for hypertension management using the Korea National Health Insurance Corporation database(the insureds’ screening and health care benefit data). This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques on hypertension care predictive model and hypertension care compliance segmentation model was developed by Decision tree analysis. This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation’s building of a Hypertension follow up Management System in the near future by bringing forth representative results on the rise and care of hypertension.

Developing the High-Risk Drinking Scorecard Model in Korea

Objectives This study aimed to develop a high-risk drinking scorecard using cross-sectional data from the 2014 Korea Community Health Survey. Methods Data were collected from records for 149,592 subjects who had participated in the Korea Community Health Survey conducted from 2014. The scorecard model was developed using data mining, a scorecard and points to double the odds approach for weighted multiple logistic regression. Results This study found that there were many major influencing factors for high-risk drinkers which included gender, age, educational level, occupation, whether they received health check-ups, depressive symptoms, over-moderate physical activity, mental stress, smoking status, obese status, and regular breakfast. Men in their thirties to fifties had a high risk of being a drinker and the risks in office workers and sales workers were high. Those individuals who were current smokers had a higher risk of drinking. In the scorecard results, the highest score range was observed for gender, age, educational level, and smoking status, suggesting that these were the most important risk factors. Conclusion A credit risk scorecard system can be applied to quantify the scoring method, not only to help the medical service provider to understand the meaning, but also to help the general public to understand the danger of high-risk drinking more easily.

A Statistical Analysis of Professional Baseball Team Data: The Case of the Lotte Giants

Abstract Knowing what factors into a player’s ability to affect the outcome of a sports game is crucial. This knowledgehelps determine the relative degree of contribution by each team member as well as sets appropriate annualsalaries. This study uses statistical analysis to investigate how much the outcome of a professional baseballgame is influenced by the records of individual players. We used the Lotte Giants’ data on 252 games playedbetween 2007 and 2008 that included environmental data(home or away games and opponents) as well aspitchers’ and batters’ data. Using a SAS Enterprise Miner, we performed a logistic regression analysis anddecision tree analysis on the data. The results obtained through the two analytic methods are comparedand discussed. Keywords: Decision tree analysis, logistic regression analysis, odds ratio, SAS Enterprise Miner. 1. Introduction The Korean Baseball League(KBL) was founded in 1982. The Lotte Giants(a professional baseballteam based in Busan, Korea) is one of the original franchises of the Korea Baseball Organiza-tion(KBO) league. The Lotte Giants won the league championship in 1984 and 1992, and weretwice the league runner-ups, in 1995 and 1999. Between 1982 and 2008, they played a total of3,263 games, 1,464 of which they won, 1,715 of which they lost, and 84 of which they tied, for awinning percentage of 0.449. In 2009, the Lotte Giants defended their title as Korea’s most popularprofessional baseball club and their total attendance increased to 1,380,018.There is existing literature of statistical analysis on Korean professional baseball data, for instance,Cho and Cho (2003) analyzed the Beane Count of baseball teams and its correlation with the winningaverage. They performed a cluster analysis and a regression analysis using the data of homerunsscored and allowed, and walks earned and allowed, which are the basic components of the BeaneCount. Kim (2004), meanwhile, pointed out how the criteria used for determining team rankings

Estimation for the Triangular Distribution under Progressive Type-II Censoring

In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a triangular distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation for various progressive censoring schemes.