• Journal of Korean Society of Transportation
• /
• v.26 no.1
• /
• pp.113-126
• /
• 2008

This research aims to explore the effects of multicollinearity on the reliability and goodness of fit of logit model. To investigate the effects of multicollinearity on the multinominal logit model, numerical experiments are performed. The exploratory variables(attributes of utility functions) which have a certain degree of correlations from (rho=) 0.0 to (rho=) 0.9 are generated and rho-squares and t-statistics which are the indices of goodness of fit and reliability of logit model are traced. From the well designed numerical experiments, following findings are validated : 1) When a new exploratory variable is added, some of rho-squares increase while the others decrease. 2) The higher relations between generic variables lead a logit model worse with respect to goodness of fit. 3) Multicollinearity has a tendency to produce over-evaluated parameters. 4) The reliability of the estimated parameter has a tendency to decrease when the correlations between attributes are high. These results suggest that we have to examine the existence of multicollinearity and perform the proper treatments to diminish multicollinearity when we develop logit model.

• The Korean Journal of Applied Statistics
• /
• v.21 no.4
• /
• pp.589-602
• /
• 2008

This article studies on disability pensioners' characteristic with multinomial logit and logistic regression model. Seven factors are examined on whether each factor is reflected in degree of disability in the disability pension. By incorporating multinomial logit and logistic regression model, effectiveness and characteristic of the seven factors are investigated on the degree of disability. Result shows all the seven factors are significant on the degree of disability, while among the seven, five factors, age, sex, type of coverage, type of category, insured duration show a trend in degree of disability and the other two, cause of disability and class of standard monthly income are not effective on trend in degree of disability. Results from analyses might be useful for disability pension management.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.889-902
• /
• 2013

Several goodness-of-fit test statistics have been proposed for a multinomial logit regression model; however, the properties of the proposed tests were not adequately studied. This paper evaluates three different goodness-of-fit tests using grouping strategies, proposed by Fagerland et al. (2008), Bull (1994), and Pigeon and Heyse (1999). In addition, Pearson (1900)'s method is also examined as a reference. Simulation studies were conducted to evaluate the four methods in terms of null distribution and power. A real data example is presented to illustrate the methods.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.923-932
• /
• 2013

This paper proposes a method to predict the number of foodborne disease outbreaks by microbes. The weekly data of food poisoning occurrences by microbes in Korea contain many zero-valued observations and have dependency between outbreaks. In order to model both phenomena, the number of food poisonings is predicted by an autoregressive model and the probabilities of food poisoning occurrences by microbes (given the total of food poisonings) are estimated by the baseline category logit model. The predicted number of foodborne disease outbreaks by a microbe is obtained by multiplying the predicted number of foodborne disease outbreaks and the estimated probability of the food poisoning by the corresponding microbe. The mean squared error and the mean absolute value error are evaluated to compare the performances of the proposed method and the zero-inflated model.

• Journal of Korean Society of Transportation
• /
• v.28 no.6
• /
• pp.43-54
• /
• 2010

In recent years there have been studies on tour based approaches for freight travel demand modelling. The purpose of this paper is to analyze tour type choice behavior of commercial vehicles which are divided into round trips and chained tours. The methods of the study are based on the decision tree and the logit model. The results indicates that the explanation variables for classifying tour types of commercial vehicles are loading factor, average goods quantity, and total goods quantity. The results of the decision tree method are similar to those of logit model. In addition, the explanation variables for tour type classification of small trucks are not different from those for medium trucks', implying that the most important factor on the vehicle tour planning is how to load goods such as shipment size and total quantity.

• The Korean Journal of Applied Statistics
• /
• v.18 no.3
• /
• pp.701-712
• /
• 2005

The suppression for logistic regression models has been debated no longer than that for linear regression models since, among many other reasons, sum of squares for regression (SSR) or coefficient of determination ($R^2$) could be defined into various ways. Based on four kinds of $R^2$'s: two kinds are most preferred, and the other two are proposed by Liao & McGee (2003), four kinds of SSR's are derived so that the suppression for logistic models is explained. Many data fitted to logistic models are generated by Monte Carlo method. We explore when suppression happens, and compare with that for linear regression models.

• Journal of Information Management
• /
• v.42 no.1
• /
• pp.199-219
• /
• 2011

Currently, managers and researchers have come to recognize the importance of knowledge as an asset to the organization. Knowledge Management System (KMS) is developed as a tool to manage the organizational knowledge. A significant literature about Knowledge Management in the private, but not public sector. The purpose of the study is to analyze the influencing factors for KMS utilization in a public sector, Jeju Provincial Government Organization. This study performed an empirical research on factors that affect the use of KMS using Knowledge Management system capability and individual & organizational capability criteria. Survey was conducted with 150 employees from Jeju Provincial Government Organization. The Results show that an ability to create knowledge, and knowledge infrastructure are significant factors in determining the level of usage of Knowledge Management system. In particular, the most important factor that influences the usage of Knowledge Management is knowledge creation. and then knowledge infrastructure. The result also shows that the usage of KMS is affected by management's support willingness.

• The Korean Journal of Applied Statistics
• /
• v.31 no.3
• /
• pp.367-382
• /
• 2018

Prediction models for a corporate bond rating in existing studies have been developed using various models such as linear regression, ordered logit, and random forest. Financial characteristics help build prediction models that are expected to be contained in the assigning model of the bond rating agencies. However, the ranges of bond ratings in existing studies vary from 5 to 20 and the prediction models were developed with samples in which the target companies and the observation periods are different. Thus, a simple comparison of the prediction accuracies in each study cannot determine the best prediction model. In order to conduct a fair comparison, this study has collected corporate bond ratings and financial characteristics from 2013 to 2017 and applied prediction models to them. In addition, we applied the elastic-net penalty for the linear regression, the ordered logit, and the ordered probit. Our comparison shows that data-driven variable selection using the elastic-net improves prediction accuracy in each corresponding model, and that the random forest is the most appropriate model in terms of prediction accuracy, which obtains 69.6% accuracy of the exact rating prediction on average from the 5-fold cross validation.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.373-380
• /
• 2013

Recently, AR(Augmented Reality) system as an information technology for the increased access of information has a potential possibility of next generation's system for tourism guide. In this regard, the objective of this study is to explore the technology acceptance factors of AR system on tourism destination. To achieve the objective of this study, logit regression model was used to analyze the influential level of the factors. This study was analyzed with the final 224 respondents and the results showed that if there will be assured with high trust and easy access via mobility device as smartphone, the AR system has the possibility of high acceptance level. The result of this study will be expected to be utilized as fundamental data from the viewpoint of the service providers and system developers that want to launch the appropriate service to users' needs of AR system.

• The Korean Journal of Applied Statistics
• /
• v.25 no.2
• /
• pp.293-300
• /
• 2012

When Binomial data are obtained, logit and logit mixed models are commonly used for small area estimation. Those models are known to have good statistical properties through the use of unit level information; however, data should be obtained as area level in order to use area level information such as spatial correlation or auto-correlation. In this research, we suggested a new small area estimator obtained through the combination of unit level information with area level information.