• Journal of the Korean Data and Information Science Society
• /
• 제19권2호
• /
• 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 the Korean Statistical Society
• /
• 제12권1호
• /
• pp.24-35
• /
• 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.

• 대한교통학회지
• /
• 제34권6호
• /
• pp.570-579
• /
• 2016

Stepwise 환승계수(이하 STC)는 환승회수 증가에 따라 환승비용을 실제보다 많게 인식하도록 한다. 통행시간과 최소환승회수 정보를 제공하는 것은 이러한 경향을 감안하는 취지이다. STC가 포함된 경로탐색문제는 비가산성비용을 포함하며 최적조건의 비성립으로 경로열거가 요구된다. 따라서 대중교통망에서 STC를 고려하는 경로탐색에 대한 이론적 검토를 통해서 경로탐색의 실패를 우회하는 방안이 요구된다. 본 연구는 STC가 포함되는 대중교통망에서 확률적 통행배정모형에 대하여 검토한다. 비가산성 경로문제를 완화하는 방안으로 유입링크기반 전체경로삭제기법을 활용하는 방안을 제안한다. 전체경로삭제기법은 출발지에서 도착지까지 서로 상이한 경로의 구성된 가능경로집합을 구축하기 용이하다. STC를 반영한 경로기반 Logit 모형을 대중교통통행배정기법으로 구축한다.

• Industrial Engineering and Management Systems
• /
• 제11권2호
• /
• 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.

• 한국콘텐츠학회논문지
• /
• 제11권8호
• /
• pp.459-467
• /
• 2011

버스이용 활성화를 목적으로 구축되어진 버스정보시스템(Bus Information Systems)은 정류소에 설치한 버스안내단말기(Bus Information Terminal)를 통하여 버스대기자에게 버스도착정보 등을 제공하고 있다. BIT는 도심곳곳에 산재한 정류소에 설치되어 시민들이 직접 사용하는 장치이므로 고장 파손 등의 신속한 대응이 BIS운영에 중요한 요소로 인식된다. 이에 본 논문에서는 이러한 유지보수비용을 자체적으로 충당할 목적으로 BIT화면에 민간광고를 게재할 경우를 가정한 후, 조건부가치측정법(Contingent Valuation Method)을 적용하여 적정수준의 광고료를 파악하였다. 또한, 다항로짓모형(Multinomial Logit Medel)을 이용하여 버스이용자시간대별 이용자 특성을 분석하였으며, 이용자의 성별 및 연령을 고려한 시간대별 BIT광고게재 전략을 제시하였다.

• Communications for Statistical Applications and Methods
• /
• 제10권2호
• /
• pp.479-496
• /
• 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.

• Journal of the Korean Statistical Society
• /
• 제26권4호
• /
• pp.493-505
• /
• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• 패션비즈니스
• /
• 제6권5호
• /
• pp.136-150
• /
• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• 보건행정학회지
• /
• 제15권4호
• /
• pp.81-109
• /
• 2005

According to many recent studies suggesting that cash flow analysis method tends to be more effective than traditional financial index analysis method to predict corporate bankruptcy, this study applies the cash flow analysis method to hospital business to identify the significant variables which can distinguish between superior hospitals and bankruptcy hospitals. The author analyzed recent 3 years, i.e. from the year of 2000 to the year of 2002, financial statements of 31 bankrupt hospitals In 2003, and the same number of superior hospitals through using Multiple Discriminant Analysis and Logit Analysis. The results are belows; First, the study releases that Logit Analysis is more likely to be effective than Multiple Discriminant Analysis. Second, this research also shows that traditional financial index analysis method is more superior compare to cash flow analysis method for hospital bankruptcy predict model. Finally, this study suggest that the significant variables, which can distinguish superior hospitals from bankrupt hospitals, are Operating/Current Liabilities$(Y_2)$, CFO/Equity$(Y_5)$ for cash flow analysis method and Net Worth to Total Assets Ratio$(X_1)$, Quick Ratio $(X_3)$, Return on Assets$(X_6)$, Growth Rate of Patient Revenues$(X_{16})$ for traditional financial index analysis method.

• Journal of Information Technology Applications and Management
• /
• 제13권4호
• /
• pp.273-290
• /
• 2006

This study suggests a frequency matrix technique to predict personal credit rate more efficiently using incomplete data sets. At first this study test on multiple discriminant analysis and logistic regression analysis for predicting personal credit rate with incomplete data sets. Missing values are predicted with mean imputation method and regression imputation method here. An artificial neural network and frequency matrix technique are also tested on their performance in predicting personal credit rating. A data set of 8,234 customers in 2004 on personal credit information of Bank A are collected for the test. The performance of frequency matrix technique is compared with that of other methods. The results from the experiments show that the performance of frequency matrix technique is superior to that of all other models such as MDA-mean, Logit-mean, MDA-regression, Logit-regression, and artificial neural networks.