• Journal of Distribution Science
• /
• v.17 no.11
• /
• pp.73-79
• /
• 2019

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.5
• /
• pp.62-70
• /
• 2016

Ensuring walkability in a city where pedestrians and vehicles coexist is an issue of critical importance. The relative relationship between vehicle transit and walkability improvements complicates the evaluation of walkability, which thus necessitates the formation of a quantitative standard by which a methodological measurement of walkability can be achieved inside the pedestrian network. Therefore, a model is determined whereby quantitative indices such as, but not limited to, experiences of accessibility, mobility, and convenience within the network are estimated. This research proposes the integration of space syntax theory and the logit path choice model in the evaluation of walkability. Space syntax theory assesses adequacy of the constructed pedestrian network through calculation of the link integration value, while the logit model estimates its safety, mobility, and accessibility using probability. The advantage of the integrated model hence lies in its ability to sufficiently reflect such evaluation measures as the integration value, mobility convenience, accessibility potential, and safety experienced by the demand in a quantitative manner through probability computation. In this research, the Dial Algorithm is used to arrive at a solution to the logit model. This process requires that the physical distance of the pedestrian network and the perceptive distance of space syntax theory be made equivalent. In this, the research makes use of network expansion to reflect wait times. The evaluation index calculated through the integrated model is reviewed and using the results of this sample network, the applicability of the model is assessed.

• Industrial Engineering and Management Systems
• /
• v.11 no.2
• /
• pp.148-154
• /
• 2012

Micro Electric Vehicles are considered as a solution for reducing $CO_2$ emissions, however, it is difficult to evaluate its impact in a local community when it has been introduced. In this study, we evaluated how to spread the Micro EV within the community, using the utility derived from a multinomial logit model, and analyze the effect on $CO_2$ emissions. The householder's utility model is based on an investigation about Kiryu citizen's activities of shopping, transportation methods, etc. Using the geographic information system, we get the distances of each householder and the stores, and estimate a multinomial logit model about the combination choices of shopping stores and transportation method.

• Journal of Korean Society of Transportation
• /
• v.21 no.4
• /
• pp.57-66
• /
• 2003

Because the Logit model easily calculates probabilities for choice alternatives and estimates parameters for explanatory variables, it is widely used as a traffic mode choice model. However, this model includes an assumption which is independently and identically distributed to the error component distribution of the mode choice utility function. This paper is a study on the estimation of the Heteroscedastic Logit Model. which mitigates this assumption. The purpose of this paper is to estimate a Logit model that more accurately reflects the mode choice behavior of passengers by resolving the homoscedasticity of the model choice utility error component. In order to do this, we introduced a scale factor that is directly related to the error component distribution of the model. This scale factor was defined so as to take into account the heteroscedasticity in the difference in travel time between using public transport and driving a car, and was used to estimate the travel time parameter. The results of the Logit Model estimation developed in this study show that Heteroscedastic Logit Models can realistically reflect the mode choice behavior of passengers, even if the difference in travel time between public and private transport remains the same as passenger travel time increases, by identifying the difference in mode choice probability of passengers for public transportation.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.2
• /
• pp.413-420
• /
• 2008

This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.

• Journal of applied mathematics & informatics
• /
• v.3 no.2
• /
• pp.253-264
• /
• 1996

The generalized logit model of nominal type with random regressors is studied for bootstrapping. We assess the accuracy of some estimators for our generalized logit model using a Monte Carlo simu-lation. That is we study the finite sample properties containing the consistency and asymptotic normality of the maximum likelihood es-timators. Also we compare Newton Raphson algorithm with BHHH algorithm.

• Communications for Statistical Applications and Methods
• /
• v.9 no.1
• /
• pp.281-289
• /
• 2002

In this paper, we considered an application of the bootstrap method for logit model. Estimation of type I error probability, the bootstrap p-values and bootstrap confidence intervals of parameter were proposed. Small sample Monte Carlo simulation were conducted in order to compare proposed method with existing normal theory based asymptotic method.

• Journal of Korean Society of Transportation
• /
• v.13 no.4
• /
• pp.61-77
• /
• 1995

The main objective of this research is the development of a logit model of urban workers' auto ownership choice. For the utility specification. a variety of behavioral hypotheses about the factors which affect the urban workers' auto ownership choice are considered. Based on the behavioral hypotheses, a binary logit model of auto ownership is estimated. Empirical estimation is based on a sample of workers taken in Daegu City(1994). The binary logit model of auto ownership development in this paper provides reasonable results in terms of behavioral and statistical considerations. Furthermore, this paper develops several submarket models of auto ownership choice. Market segmentation was made using age, sex, income, home-to-work time distance. It is found that the estimated results with market segmentation are also reasonable. Finally future directions of model development are suggested.

• Journal of the Korean Society for Railway
• /
• v.14 no.4
• /
• pp.383-389
• /
• 2011

Until now, in planning and constructing KTX and the Express Way, the connectivity and transfer between these facilities have not been considered. In this study the effect of mode choice behavior by connecting KTX and the Express Way is analyzed through estimating Multinomial Logit Model and Binary Logit Model. The SP and RP surveys to develop these models were carried out and the data were selected from the passengers using the KTX station, Express Bus Terminals and Rest Areas in the Express Way. To test the effect of connectivity and transfer in the field, the case study for Dongtan KTX station was carried out. According to the results, connecting the KTX station and the Express Way has the effect of increasing the demand by 30%. And this is caused by saving about 120 minutes of traveling time from Seoul to Pusan. This study shows that the connectivity and transfer can increase the efficiency of transportation system and the improvement in the mobility and accessibility will maximize the usages of these two facilities.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.1
• /
• pp.123-130
• /
• 2006

This paper discusses about how to build up a mixed-effects model using cumulative logits when some factors are fixed and others are random. Location effects are considered as random effects by choosing them randomly from a population of locations. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.