• Communications for Statistical Applications and Methods
• /
• 제15권2호
• /
• pp.229-234
• /
• 2008

A Cook-type distance is considered for investigating the influence of observations in constrained regression models. Its exact sampling distribution is derived, which is used for judging whether each observation is influential or not. A numerical example is provided for illustration.

• Journal of the Korean Statistical Society
• /
• 제23권2호
• /
• pp.463-482
• /
• 1994

In Bayesian analysis of randomized response models, the likelihood function does not combine tractably with typical priors for the parameters of interest, causing computational difficulties in posterior analysis of the parameters of interest. In this article, the difficulties are solved by introducing appropriate latent variables to the model and using the Gibbs sampling algorithm.

• Communications for Statistical Applications and Methods
• /
• 제14권2호
• /
• pp.281-286
• /
• 2007

Cook's distance is generalized to the multiple linear regression with linear constraints on regression coefficients. It is used for identifying influential observations in constrained regression models. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
• /
• 제18권1호
• /
• pp.71-77
• /
• 2011

In this paper, asymptotic results are investigated when a parametric transformation is applied to ARMA models. The conditions are determined to ensure the strong consistency and the asymptotic normality of maximum likelihood estimators and the correct coverage probability of the forecast interval obtained by the transformation and backtransformation approach.

• Journal of the Korean Statistical Society
• /
• 제2권1호
• /
• pp.3-7
• /
• 1973

Zellner, Kmenta, Dreze (1966) and later Hedges (1969) showed that consistent estimates of the parameters of Cobb-Douglas or CES production functions can be obtained by the single equation estimation methods if the models incorporate the assumption that firms maximize the mathematical expectation of profits. This note demonstrates that the results of the above-cited works can be extended to a class of VES production function models.

• Journal of the Korean Statistical Society
• /
• 제32권1호
• /
• pp.1-10
• /
• 2003

We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

• 보건행정학회지
• /
• 제9권2호
• /
• pp.1-20
• /
• 1999

This study was conducted to investigate the predictors of hospital bankruptcy in Korea and to examine the predictive power for 3 types of statistical models of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 30 bankrupt and 30 profitable hospitals in 1. 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the predictive power of statistical models of hospital bankruptcy were compared. The major findings are as follows. 1. Nine out of 21 indicators - fixed ratio, quick ratio, operating profit to total assets, operating profit to gross revenue, normal profit to total assets,normal profit to gross revenue, net profit to gross revenue, inventories turnrounds, and added value per adjusted patient - were found to be significantly predictitive variables in Logit and Probit models. 2. The predicdtive power of discriminant model of hospital bankruptcy in 1. 2, and 3 years before bankruptcy were 85.4, 79.0, and 83.8% respectively. With regard to the predictive power of the Logit model of hospital bankruptcy, they were 82.3, 75.8, and 80.6% respectively, and of the Probit model. 87.1. 80.6, and 88.7% respectively. 3. The predictive power of the Probit model of hospital bankruptcy is better than the other two predictive models.

• 한국도로학회논문집
• /
• 제14권5호
• /
• pp.189-199
• /
• 2012

PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

• 응용통계연구
• /
• 제2권2호
• /
• pp.37-45
• /
• 1989

불균형경계량경제모형의 분석을 위하여는 부분조정모형 또는 교체회귀모형이 응용되고 있다. 본 연구에서는 교체회귀모형이 표본분리 여부에 따라, 또 교체의 원인이 외생적인지 내생적인지에 따라 어떻게 구분될 수 있는지 보이고, 단일시장을 주 대상으로 하여 표본분리가 되는 경우와 그렇지 않은 경우로 나누어 각각의 추정방법과 그에 따른 문제점을 설명하였다.

• Ocean Systems Engineering
• /
• 제5권1호
• /
• pp.41-54
• /
• 2015

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.