• Title, Summary, Keyword: Logit Model

Search Result 601, Processing Time 0.047 seconds

Analysis of Multicategory Responses with Logit Model on Earlyold Age Pension

  • Kim, Mi-Jung
    • Journal of the Korean Data and Information Science Society
    • /
    • v.19 no.3
    • /
    • pp.735-749
    • /
    • 2008
  • This article suggests application of logit model for analysis of multicategory responses. Referring to the reference category, characteristic of each category is obtained from analysis of polytomous logit model. With National Pension data it is illustrated that application of logit model helps it possible to find significant factors which may not be found only with polytomous logit model. Application of the logit model is done by reducing the number of categories. Categories are grouped into the former and the latter group according to reference category. Extra finding of significant factor was possible from logistic regression analysis for the two groups after removing the reference category. It is expected that this application would be helpful for finding information and characteristics on ordered multicategory responses where the proportional odds model does not fit.

  • PDF

Limits of Logit Models in Transportation Policy Evaluation : Expected Utilities in Logit Models (교통정책평가에 있어 Logit모형의 한계 : Logit모형에 있어서의 기대효용)

  • 조중래
    • Journal of Korean Society of Transportation
    • /
    • v.5 no.1
    • /
    • pp.25-31
    • /
    • 1987
  • This article shows that, in the logit models, the(conditional) expected utility of the decision makers choosing an alternative is invariant across all alternatives. This property of the logit model implies that the logit model can not explain the distributional wealfare effects of a transportation policy (or transportation investment) among different alternatives, and thus the logit model is not proper for evaluating transportation policy in equity aspects.

  • PDF

The Confidence Intervals for Logistic Model in Contingency Table

  • Cho, Tae-Kyoung
    • Communications for Statistical Applications and Methods
    • /
    • v.10 no.3
    • /
    • pp.997-1005
    • /
    • 2003
  • We can use the logistic model for categorical data when the response variables are binary data. In this paper we consider the problem of constructing the confidence intervals for logistic model in I${\times}$J${\times}$2 contingency table. These constructions are simplified by applying logit transformation. This transforms the problem to consider linear form which called the logit model. After obtaining the confidence intervals for the logit model, the reverse transform is applied to obtain the confidence intervals for the logistic model.

Application of Logit Model in Qualitative Dependent Variables (로짓모형을 이용한 질적 종속변수의 분석)

  • Lee, Kil-Soon;Yu, Wann
    • Journal of Korean Home Management Association
    • /
    • v.10 no.1
    • /
    • pp.131-138
    • /
    • 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.

  • PDF

Effects of Multicollinearity in Logit Model (로짓모형에 있어서 다중공선성의 영향에 관한 연구)

  • Ryu, Si-Kyun
    • 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.

Destination Choice Behavior for Recreation Areas : Application of Generalized Logit Models (서울시내와 근교에 위치한 당일여가용 Recreation시설의 선택행동 확정에 관한 연구 : Generalized Logit Model의 적용)

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

  • PDF

Bootstrapping Logit Model

  • Kim, Dae-hak;Jeong, Hyeong-Chul
    • 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.

A Review on Marketing Models' Implications to Market Positioning: With a Focus on the Hauser and Shugan Model (마케팅 모형의 포지셔닝 관련 시사점에 대한 고찰: Hauser and Shugan 모형을 중심으로)

  • Won, Jee-Sung
    • The Journal of Distribution Science
    • /
    • v.14 no.11
    • /
    • pp.61-73
    • /
    • 2016
  • Purpose - Marketing scholars have developed various types of mathematical models for describing marketing phenomenon, because there is no single model comprehensive enough to incorporate all the relevant marketing phenomena. This study tries to summarize the behavioral foundations and the mathematical derivations of the most widely used marketing models and discusses their strategic implications. This study selected four representative marketing models: multinomial logit(MNL) model, elimination-by-aspects(EBA) model, Hauser and Shugan model and Bass diffusion model. Especially, this study focuses on Hauser and Shugan(1983)'s Defender model and discusses the model's behavioral foundation and its implications. Research design, data, and methodology - Of the four selected model, the multinomial logit model is selected as the basic normative model and the other three models are described as descriptive models in contrast. Starting the discussion from the multinomial logit model, this study explains what important strategic variables are incorporated in each of the four models. The IIA(independence of irrelevant alternatives) axiom and Luce choice model is also discussed in relation to the multinomial logit model. The concept of 'efficient frontier' is discussed in relation to Hauser and Shugan's model. Graphs and tables are used to represent the key implications. No empirical study is included. Results - The analyses of the mathematical marketing models are shown to be very useful in understanding the essence of positioning strategy. The multinomial logit model implies the importance of increasing utility or consumer preference level. The EBA model implies the importance of lowering the inter-brand similarity and dominating the competitors. Hauser and Shugan model implies the importance of considering customer heterogeneity distribution in selecting the target market. Conclusions - It is shown that the concepts of 'efficient frontier' is useful in understanding the effectiveness of positioning strategy. Market positioning can be understood as occupying some place on the efficient frontier. The important strategic implications can be summarized as follows: Always try to increase customer preference by providing what they value, and differentiate from competing alternatives as much as possible. The best positioning strategy is to dominate all the competitors and the worst is to be dominated by the competitors.

Laplace-Metropolis Algorithm for Variable Selection in Multinomial Logit Model (Laplace-Metropolis알고리즘에 의한 다항로짓모형의 변수선택에 관한 연구)

  • 김혜중;이애경
    • Journal of the Korean Society for Quality Management
    • /
    • v.29 no.1
    • /
    • pp.11-23
    • /
    • 2001
  • This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

  • PDF

On an Optimal Bayesian Variable Selection Method for Generalized Logit Model

  • Kim, Hea-Jung;Lee, Ae Kuoung
    • Communications for Statistical Applications and Methods
    • /
    • v.7 no.2
    • /
    • pp.617-631
    • /
    • 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.

  • PDF