• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.135-145
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• 2016

This study assumes a vehicle choice model based on the multinomial model and analyzes the vehicle choice behaviors of consumer. An SP survey targeting drivers was implemented and data was collected for model estimates, with the possible choice options of the survey takers limited to gasoline, HEV, PHEV, and EV vehicles. The explanatory variable mostly displayed a significance level of under 5%, and excluding variables for price and fuel the remaining variables were all consistent with the logical direction with the plus (+) sign and the results were determined to be rational. Consumers selecting mid-size & full-size vehicles are able to afford more than consumers that selected other vehicle types, so there was relatively little consideration given to low fuel costs when compared to vehicle price. For this reason, it was determined that for the full-size vehicle model the fuel variable could be disregarded. Socio-economic variables that were statistically significant were the age and infor variables for the sub-compact & compact, the age, infor and inc3 variables for the mid-sized & full-size vehicles.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.479-496
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• 2003

We investigate logit confidence intervals for the odds ratio based on the delta method. These intervals are constructed using pseudo-Bayes estimators. The Gart method and Agresti method smooth the observed counts toward the model of equiprobability and independence, respectively. We obtain better coverage probability by smoothing the observed counts toward the pseudo-Bayes estimators in 2$\times$2 table. We also improve legit confidence intervals in 2$\times$2$\times$K tables by generalizing these ideas. Utilizing pseudo-Bayes estimators, we obtain better coverage probability by smoothing the observed counts toward the conditional independence model, no three-factor interaction model and saturated model in 2$\times$2$\times$K tables.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.655-673
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• 2004

This paper is concerned with analysing the bankruptcy prediction power of three models: Multivariate Discriminant Analysis(MDA ), Logit Analysis, Neural Network. The after-crisis bankrupted companies were limited to the research data and the listed companies belonging to manufacturing industry was limited to the research data so as to improve prediction accuracy and validity of the model. In order to assure meaningful bankruptcy prediction, training data and testing data were not extracted within the corresponding period. The result is that prediction accuracy of neural network model is more excellent than that of logit analysis and MDA model when considering that execution of testing data was followed by execution of training data.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.81-96
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• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.123-136
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• 2020

It is common to encounter correlated multiple outcomes measured on the same subject in various research fields. In developmental toxicity studies, presence of malformed pups and fetal weight are measured on the pregnant dams exposed to different levels of a toxic substance. Joint analysis of such two outcomes can result in more efficient inferences than separate models for each outcome. Most methods for joint modeling assume a normal distribution as random effects. However, in developmental toxicity studies, the response distributions may change irregularly in location and shape as the level of toxic substance changes, which may not be captured by a normal random effects model. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint model for binary and continuous outcomes. In our model, we incorporate a skewed logit model for the binary outcome to allow the response distributions to have flexibly in both symmetric and asymmetric shapes on the toxic levels. We apply our proposed method to data from a developmental toxicity study of diethylhexyl phthalate.

• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.459-467
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• 2011

BIS(Bus Information Systems) supplies the bus arrived time information for users in using BIT(Bus Information Terminal) installed on the bus stop. BIT is the device using peoples directly. So, BIT need a quick response when it flew. These are an important factor in the strategy of the BIS maintenance. BIT need a maintenance cost to operate smoothly. So, Suppose that commercial advertisement can be displayed on BIT screen in this study. And we researched an advertisement rates of the optimum level in using Contingent Valuation Method. In addition, we analyzed a characteristic of user's depending on each time using multinomial Logit Modeling method, and studied for BIT operation and ad. displaying strategy considered user's sex, ages and using times.

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

• Journal of the Korean association of regional geographers
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• v.13 no.6
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• pp.712-723
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• 2007

The objective of this paper is to search the factors inducing the visitors to revisit in Bangeo Festival of Jeju region. To get this objective, this study analyzed the data with the Multinomial Logit Model applied dependent variable to intention of revisit. As a result, 'festival program' factor is the most important thing and if the factor increases by 1 unit, the probability of 'revisit' can be increased by 5.255 times than the probability of 'no revisit'. Secondly, the next significant factors are 'festival convenience' and 'festival recognition in advance'. So the providers of the festival will intend to prepare the festival focused on the important factor proposed by this results.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.163-168
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• 2009

Travel time reduction is a benefit in economic analysis. Freight transportation time reduction benefits influence the logistics industry. The objectives of this paper are to estimate the Value of Time (VOT) for transportation time reduction with logit methodology. The data of Gyeonggi-do's domestic road freight transport in 2007 are used. VOT was estimated for five commodities. An average VOT of 19,946 won/vehicle-hr was calculated; transport of electronic parts had the highest VOT. This study will help provide direction for improving Korea's road infrastructure for freight.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.108-117
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• 2007