• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.605-616
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• 2000

Three primary interests frequently raised by mortgage companies are introduced and the corresponding statistical approaches for the default probability in mortgage companies are examined. Statistical models considered in this paper are time series, logistic regression, decision tree, neural network, and discrete time models. Usage of the models is illustrated using an artificially modified data set and the corresponding models are evaluated in appropriate manners.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.125-134
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• 2003

Standard estimation methods for linear mixed models are sensitive to influential observations. However, tools and concepts for linear mixed model diagnostics are rudimentary until now and research is heavily demanded in linear mixed models. In this paper, we consider two diagnostics to evaluate the effects of individual observations in the estimation of fixed effects for linear mixed models. Those are Cook's distance and COVRATIO. Results of our limited simulation study suggest that the Cook's distance is not good statistical quantity in linear mixed models. Also calibration point for COVRATIO seems to be quite conservative.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.523-532
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• 1999

Generalized linear models have various applications for data arising from many kinds of statistical studies. Although the response variable is generally assumed to be generated from a wide class of probability distributions we focus on count data that are most often analyzed using binomial models for proportions or poisson models for rates. The methods and results presented here also apply to many other categorical data models in general due to the relationship between multinomial and poisson sampling. The novelty of the approach suggested here is that all conditional distribution s can be specified directly so that staraightforward Gibbs sampling is possible. The prior distribution consists of two stages. We rely on a normal nonconjugate prior at the first stage and a vague prior for hyperparameters at the second stage. The methods are demonstrated with an illustrative example using data collected by Rosenkranz and raftery(1994) concerning the number of hospital admissions due to back pain in Washington state.

• Journal of Korean Academy of Nursing
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• v.30 no.6
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• pp.1503-1520
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• 2000

The purpose of the present study is 1) to explain major effect models (main, moderating, and mediating) of social support and statistical methods for testing the effect models and 2) to analyze and evaluate the consistency in the use of the effect models and its statistical methods in Korean nursing studies. A total of 57 studies were selected from Journal of Korean Academy of Nursing, Journal of Korean Academic Society of Adult Nursing, Journal of Korean Women's Health Nursing Academic Society, Journal of Fundamentals of Nursing, Journal of Korean Community Nursing, Journal of Korean Psychiatric and Mental Health Nursing Academic Society, and Journal of Korean Pediatric Nursing Academic Society published in the year of 1990-1999. In results, most studies on social support performed in Korea Nursing Society were about a main effect model. There are few studies on moderating or mediating model of social support. Thus, it was difficult to find research findings how, why, under what conditions social support impacted on health outcomes. Most studies on the moderating or mediating effect model of social support used statistical methods for testing main effect model rather than for testing moderating or mediating effect model. That is, there are inconsistency between effect models of social support and its statistical methods in Korean nursing researches. Therefore, it is recommended to perform studies on moderating or mediating effect model and use appropriate statistical methods.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.161-166
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• 2003

In this paper, a new form of generalized linear models is proposed. The proposed models consist of a distribution function of the mean response and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models. Markov chain Monte Carlo methods are used to estimate the parameters within a Bayesian framework.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.123-136
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• 2015

Science has developed with great achievements after Galileo's discovery of the law depicting a relationship between observable variables. However, many natural phenomena have been better explained by models including unobservable random effects. A mixed effect model was the first statistical model that included unobservable random effects. The importance of the mixed effect models is growing along with the advancement of computational technologies to infer complicated phenomena; subsequently mixed effect models have extended to various statistical models such as hierarchical generalized linear models. Hierarchical likelihood has been suggested to estimate unobservable random effects. Our special issue about mixed effect models shows how they can be used in statistical problems as well as discusses important needs for future developments. Frequentist and Bayesian approaches are also investigated.

• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.45-53
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• 1990

Factorization models are a generalization of hierarchical loglinear models which apply equally to discrete and continuous distributions. In regular (strictly positive) cases the intersection of two factorization models is another factorization model whose representation is obtained by a simple algorithm. Failure of this result in an irregular case is related to a theorem of Basu on ancillary statistics.

• Advances in concrete construction
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• v.3 no.3
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• pp.161-185
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• 2015

FRP confining is a widely used method for seismic retrofitting of concrete columns. Several studies investigated the stress-strain behavior of FRP confined concrete prisms with square and rectangular sections both experimentally and analytically. In some studies, the monotonic stress-strain behavior of confined concrete was investigated and compressive strength models were developed. To study the reliability of these models, thorough statistical tests are required. This paper aims to investigate the reliability of the presented models using statistical tests including t-test, wilcoxon rank sum test, wilcoxon signed rank test and sign test with a level of significance of 5%. Wilk Shapiro test was also employed to evaluate the normality of the data distribution. The results were compared for different cross section and confinement types. To see the accuracy of the models when there were no significant differences between the results, the coefficient of confidence was used.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.585-594
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• 2003

It has been generally recognized that conventional binomial or Poisson model provides poor fits to the actual correlated binary data due to the extra-binomial variation. A number of generalized statistical models have been proposed to account for this additional variation. Among them, beta-binomial, correlated-binomial, and modified-binomial models are binomial-related models which are frequently used in modeling the sum of n correlated binary data. In many situations, it is reasonable to assume that n correlated binary data are exchangeable, which is a special case of correlated binary data. The sum of n exchangeable correlated binary data is modeled relatively well when the above three binomial-related models are applied. But the estimation results of correlation coefficient turn to be quite different. Hence, it is important to identify which model provides better estimates of model parameters(success probability, correlation coefficient). For this purpose, a small-scale simulation study is performed to compare the behavior of above three models.