Jihyun Park

Cartels cause tremendous damage to the market economy and disadvantage consumers by creating higher prices and lower-quality goods; moreover, they are difficult to detect. We need to prevent them through scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. In particular, the probability of cartel penalization is a useful indicator for evaluating competition enforcement. This study estimates the probability of cartel penalization using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by a Bayesian approach from the cartel data of the Department of Justice in the United States between 1970 and 2009. The probability of cartel penalization is seen as sensitive to changes in competition law, and the results have implications for market efficiency and the antitrust authority’s efforts against cartel formation and demise. The result of policy simulation shows the effectiveness of the leniency program. Antitrust enforcement is evaluated from the estimation results, and can therefore be improved.

Cartels cause tremendous damage to the market economy and disadvantage consumers by causing higher prices and lower quality; moreover, they are difficult to detect. We need to prevent them by scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. Particularly, the probability of cartel penalization is a useful indicator for the evaluation of the competition enforcement. This study is to estimate the probability of cartel penalization by using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by Bayesian approach from cartel data of Department of Justice in United States from 1970 to 2009. The probability of cartel penalization is seen to be sensitive to change of competition law and the results shows the usefulness of higher interpretation than other research. The result of the policy simulation shows how effective the leniency program is. From this estimation, antitrust enforcement is evaluated, and thereby, can be improved.

Weibull distribution is widely used in reliability engineering and lifetime analysis because of its flexibility in modeling both increasing and decreasing failure rate. Weibull distribution has shape parameter and scale parameter, and it is difficult to estimate the parameters due to the no-closed form of likelihood function. In recent years, there has been studied on the approximating parameter estimation methods based on the simulation. In this study, we use the approximating parameter estimation of Weibull distribution with censored lifetime. The methods which are applied in numerical example are Bayesian estimation method, maximum likelihood estimation, and Markov chain Monte Carlo. Accuracy of estimation methods is performed by the mean square errors of parameter estimator in simulation reducing the experiment time. In addition, it can be helpful to set the design of experiment considering the characteristics of Weibull distribution with censored lifetime.

Determination of a preventive maintenance scheduling is regarded as a key part in manufacturing system to maintain the equipment in good condition. In practice, many preventive maintenance policies is used in manufacturing system to reduce the unexpected failures and increase sustainability of system. In this paper, the failure rate of system is used as a condition variable and a decision variable, and preventive maintenance policy is then developed for minimizing the expected maintenance cost rate. The imperfect preventive maintenance activities of the proposed models are modeled via the arithmetic reduction model which uses the age reduction factor or the hazard reduction factor. The results of the numerical example shows that the model based on age reduction can not only extend the system lifetime but also reduce the expected maintenance cost rate although the preventive maintenance cost is significantly higher than those of the model based on hazard reduction.