• Journal of the Korean Statistical Society
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• v.12 no.1
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• pp.24-35
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• 1983

In fitting models to data, selection of the most desirable estimation method and determination of the adequacy of fitted model are the central issues. This paper compares the maximum likelihood estimators and the minimum logit chi-square estimators, both being best asymptotically normal, when logit models are fitted to infant mortality data. Chi-square goodness-of-fit test and likelihood ratio one are also compared. The analysis infant mortality data shows that the outlying observations do not necessarily result in the same impact on goodness-of-fit measures.

• Journal of Families and Better Life
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• v.10 no.1 s.19
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• pp.131-138
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• 1992

Regression analysis has become a standard statistical tool in the behavioral science. Because of its widespread popularity. regression has been often misused. Such is the case when the dependent variable is a qualitative measure rather than a continuous, interval measure. Regression estimates with a qualitative dependent variable does not meet the assumptions underlying regression. It can lead to serious errors in the standard statistical inference. Logit model is recommended as alternatives to the regression model for qualitative dependent variables. Researchers can employ this model to measure the relationship between independent variables and qualitative dependent variables without assuming that logit model was derived from probabilistic choice theory. Coefficients in logit model are typically estimated by the method of Maximum Likelihood Estimation in contrast to ordinary regression model which estimated by the method of Least Squares Estimation. Goodness of fit in logit model is based on the likelihood ratio statistics and the t-statistics is used for testing the null hypothesis.

• 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.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.281-289
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• 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 the Korean Data and Information Science Society
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• v.17 no.1
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• pp.123-130
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• 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.

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

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.57-66
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• 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 Korean Society of Transportation
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• v.13 no.4
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• pp.61-77
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• 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 Sericultural and Entomological Science
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• v.20 no.2
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• pp.36-39
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• 1978

With the current advances in insect toxicant bioassay, the need for easy methods of estimating the dosage-mortality regression equation has become vital. The Probit analysis seems to be not convenient for estimating the dosage-mortality regression equation and median lethal dose(LD50) because of its complexity in calculation. This study presents a comparision between Probit and Losit transformation for the estimation from bioassay results. Validation of the two methods is presented for the pathogenecity of nuclear polyhedrosis virus to the larva of fall web worm, Hyphantria cunea D.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.121-126
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• 2004

This paper discusses about how to build up a mixed-effects model using cumulative logits when there are some factors are fixed and others are random. Random factors are assumed to be coming from a two-way nested design for choosing individuals or experimental units to apply treatments. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.