• Journal of the Korean Institute of Landscape Architecture
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• v.22 no.3
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• pp.1-12
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• 1994

This study was carried out to identify destination choice behavior for one-day use recreation areas. Previous positioning study was utilized to select 4 study areas, and the secondary data were used for logit analyses. The Hausamn-McFadden test for IIA was conducted to examine whether conditional logit models are valid methodology for this study. The results revealed that IIA assumption among the study areas was violated; therefore, generalized binomial and generalized multinomial logit models were used in this study. In the binomial logit analysis, 2 to 5 independent variables were included in the models: their $\rho$2 values were from 0.1to 0.323, and accuracy of predictions were from 65.38 to 79.86 percent. In the multinomial logit analysis, 4 independent variables were included in the model: its $\rho$2 value was 0.207, and accuracy of prediction was 45.82 percent. The results showed that the conditional logit should be used with caution because of the IIA assumption. Several suggestions were described, mainly due to utilization of the secondary data for this study.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.1-18
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• 1995

The generalized logit model of nominal type with random regressors is studied for bootstrapping. In particular, asymptotic normality and consistency of bootstrap model estimators are derived. It is shown that the bootstrap approximation to the distribution of the maximum likelihood estimators is valid for alsomt all sample sequences.

• Journal of applied mathematics & informatics
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• v.3 no.2
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• pp.253-264
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• 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
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• v.7 no.2
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• pp.617-631
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• 2000

This paper is concerned with suggesting a Bayesian method for variable selection in generalized logit model. It is based on Laplace-Metropolis algorithm intended to propose a simple method for estimating the marginal likelihood of the model. The algorithm then leads to a criterion for the selection of variables. The criterion is to find a subset of variables that maximizes the marginal likelihood of the model and it is seen to be a Bayes rule in a sense that it minimizes the risk of the variable selection under 0-1 loss function. Based upon two examples, the suggested method is illustrated and compared with existing frequentist methods.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.2
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• pp.318-321
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• 2011

In this study, when a physician make a diagnosis of the Pattern Identifications(PIs) of stroke patients, the development methods of the PIs classification function is considered by diagnostic questionnaire of the PIs for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PIs subtypes diagnosed by two clinical experts with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PIs using the 44 items-Fire&heat(19), Qi-deficiency(11), Yin-deficiency(7), Dampness phlegm(7)- of them was significant statistically by univariate analysis in 61 questionnaires totally, we make some comparisons of the results of discriminant analysis model and generalized logit model. The overall diagnostic accuracy rate of the PIs subtypes for discriminant model(74.37%) was higher than 3% of generalized logit model(70.09%).

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

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.621-630
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• 2008

This paper discusses how to construct a generalized marginal logit model for analyzing repeated polytomous response data when some factors are applied to larger experimental units as treatments and time to a smaller experimental unit as a repeated measures factor. So, two different experimental sizes are considered. Weighted least squares(WLS) methods are used for estimating fixed effects in the suggested model.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.158-169
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• 1999

This paper is concerned with the application of generalized linear interactive modelling(GLIM) to the binary categorical data. To analyze the categorical data given by a contingency table, finding a good-fitting loglinear model is commonly adopted. In the case of a contingency table with a response variable, we can fit a logit model to find a good-fitting loglinear model. For a given $2^4$ contingency table with a binary response variable, we show the process of fitting a loglinear model by fitting a logit model using GLIM and SAS and then we estimate parameters to interpret the nature of associations implied by the model.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.379-386
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• 2004

This paper deals with a mixed logit model for vaccination data. The effect of a newly developed vaccine for a certain chicken disease can be evaluated by a noninfection rate after injecting chicken with the disease vaccine. But there are a lot of factors that might affect the noninfecton rate. Some of these are fixed and others are random. Random factors are sometimes coming from the sampling scheme for choosing experimental units. This paper suggests a mixed model when some fixed factors need to have different experimental sizes by an experimental design and illustrates how to estimate parameters in a suggested model.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.129-137
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• 2002

This paper suggests a generalized logit model with mixed effects for analysing frequency data in multi-contingency table. In this model nominal response variable is assumed to be polychotomous. When some factors are fixed but considered as ordinal and others are random, this paper shows how to use baseline-category logits to incoporate the mixed-effects of those factors into the model. A numerical algorithm was used to estimate model parameters by using marginal log-likelihood.