• Journal of the Korean Home Economics Association
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• v.33 no.4
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• pp.173-188
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• 1995

This study examined the characteristics of the relationship of HOME, sociodemographic variables and children's verbal ability at age four, five, six, Expecially this study investigated causal relationships amoong the variables which are supposed to affect children's verbal ability by children's age and sex. The subject of this study were 180 children and their mothers. Instruments included inventory of home stimulation(HOME), inventory of socio-demographic variables, inventory of the children's verbla ability. The results obtained from this study were as follows : 1. For the most part, HOME and socio-demographic variables had a significant positive correlation with children's verbal ability. 2. The variables that significantly predicted children's verbal ability differed according to children's age and sex. That is, play materials, breadth of experience and economic status of the home were predictive of boy's verbal ability at age four, while aspects of physical environment, breadth of experience were predictive at age five, fostering maturity and independence, parent's education were predictive at age six. And developmental stimulation and breadth of experience were predictive of girl's verbal ability at age four, while developmental stimulation, economic status of the home were predictive at age five, developmental stimulation and play materials were predictive at age six. 3. the results of the analysis of the causal model showed that the kind of variables that affected children's verbal ability directly differed according to children's age and sex. That is, indirect stimulation and direct stimulation affected boy's verbal ability directly at age four and five, while indirect stimulation and parent's education affected boy's verbal ability at age six. And indirect stimulation, direct stimulation, emotional climate of the home affected girl's verbal ability directly at age four, while direct stimulation, economic status of the home, indirect stimulation affected directly at age five, parent's education, indirect stimulation and direct stimulation affected girl's verbal ability at age six. 4. Another causal model of the HOME, socio-demographic variables affecting children's verbal ability showed that total HOME scores more significantly affected boys and girl's verbal ability directly than socio-demographic variables at all ages.

• Korea Journal of Hospital Management
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• v.4 no.2
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• pp.354-368
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• 1999

This research aims to compare between regression and neural network in terms of the predictive ability of the overhead costs in hospitals. For this purpose, this research uses the number of out-patients and complex medical treatments as explaining variables. Thirty-one hospitals were used for the empirical test The test result shows that the regression model has a more predictive ability than the neural network.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.383-395
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• 1999

This study is concerned with the evaluation of predictive ability of classification models with ordered multiple categories. If categories can be ordered or ranked the spread of misclassification should be considered to evaluate the performance of the classification models using loss rate since the apparent error rate can not measure the spread of misclassification. Since loss rate is known to underestimate the true loss rate the bootstrap method were used to estimate the true loss rate. thus this study suggests the method to evaluate the predictive power of the classification models using loss rate and the bootstrap estimate of the true loss rate.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.343-353
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• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.

• Korean Journal of Child Studies
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• v.8 no.1
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• pp.83-112
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• 1987

This study examined the characteristics of the relationship of home environment variables and children's intellectual ability. Two studies were conducted: Study I examined the predictability of home environment variables for children's intellectual ability by children's age and the correlations between environment variables and children's intellectual ability. Study II investigated causal relationships among the variables which are supposed to affect children's intellectual ability. The subjects of this study were 240 children at age four, six and eight attending nursery schools, kindergartens and elementary schools and their mothers. Instruments included the Inventory of Home Stimulation (HOME), inventory of sociodemographic variables, and the K-Binet scale. The results obtained from this study were as follows: 1) Home environment variables had a significant positive correlation (.36 ~ .78) with children's intellectual ability. 2) The home environmental variables that significantly predicted children's intellectual ability differed according to children's age. That is, play materials, breadth of experience, and quality of language environment were predictive of children's intellectual ability at age four, while parent's education, developmental stimulation, and play materials were predictive at age six. Economic status of the home, need gratification, avoidance of restriction, and emotional climate were predictive at age eight. 3) The causal model of home environment affecting children's intellectual ability was formulated by exogenous variables (parent education and economic status of the home) and by endogenous variables (direct stimulation, indirect stimulation and the emotional climate of the home). 4) The results of the analysis of the causal model showed that the kind of variables that affected children's intellectual ability directly differed according to children's age. That is, direct stimulation and parent's education affected children's intellectual ability directly at age four and six, while the economic status of the home and indirect stimulation affected intellectual ability directly at age eight. The amount of variance that explained children's intellectual ability increased with increase in children's age.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.48-56
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• 2005

Predictive filter theory proposed recently can be characterized by inherent advantages of estimating modelling error and overcoming the disadvantage of the Kalman filter theory. A one-step ahead error is minimized to produce optimized filter performance in the form of the predictive filter. The main advantage of this filter lies in the ability to estimate both state vector and system model error. In this paper, attitude estimation results based upon the predictive filter theory is addressed. Mathematical formulation for estimating bias signal is peformed by using the predictive filter theory, and attitude estimation based upon vector observation is presented. From the results of this study, the potential applicability of the predictive filter is highlighted.

• Management & Information Systems Review
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• v.28 no.3
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• pp.69-98
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• 2009

This study investigates in-sample and out-of-sample predictive abilities of accruals and accruals components with respect to future cash flows using models developed by Barth et al.(2001). In tests, data collected fromda62 Korean KOSPI and KOSDAQ listed firms for ccr4-2007 are used. Results of in-sample prediction tests are similar with those of Barth et al.(2001). Their accrual components model is better than other three models(NI only model, CF only model and NI-total accruals model) in future cash flows predictive ability. That is, in the case of in-sample prediction, accrual components excluding amortization have additional information contents for future cash flows. But in out-of-sample tests, the results are different. The model including operational cash flows(CF only model) shows best out-of-sample predictive ability with respect to future cash flows among above four prediction models. The accrual components model of Barth et al.(2001) has worst out-of-sample predictive ability. The results are robust to sensitivity analyses. In conclusion, we can't find the evidence that accruals and accrual components have predictive ability with respect to future cash flows in out-of-sample prediction tests. This results are consistent with results of Lev et al.(2005), and inconsistent with the belief of accounting standards formulating organizations such as FASB and KASB.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• Health Policy and Management
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• v.33 no.4
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• pp.450-456
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• 2023

Background: This study aims to develop a "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions" for the National Health Insurance Service to enhance administrative efficiency in protecting and collecting contributions from livelihood-type defaulters. Additionally, it aims to establish customized collection management strategies based on individuals' ability to pay health insurance contributions. Methods: Firstly, to develop the "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions," a series of processes including (1) analysis of defaulter characteristics, (2) model estimation and performance evaluation, and (3) model derivation will be conducted. Secondly, using the predictions from the model, individuals will be categorized into four types based on their payment ability and livelihood status, and collection strategies will be provided for each type. Results: Firstly, the regression equation of the prediction model is as follows: phat = exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction) / [1 + exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction)]. The prediction performance is an accuracy of 86.0%, sensitivity of 87.0%, and specificity of 84.8%. Secondly, individuals were categorized into four types based on livelihood status and payment ability. Particularly, the "support needed group," which comprises those with low payment ability and low-income type enrollee, suggests enhancing contribution relief and support policies. On the other hand, the "high-risk group," which comprises those without livelihood type and low payment ability, suggests implementing stricter default handling to improve collection rates. Conclusion: Upon examining the regression equation of the prediction model, it is evident that individuals with lower income levels and a history of past defaults have a lower probability of payment. This implies that defaults occur among those without the ability to bear the burden of health insurance contributions, leading to long-term defaults. Social insurance operates on the principles of mandatory participation and burden based on the ability to pay. Therefore, it is necessary to develop policies that consider individuals' ability to pay, such as transitioning livelihood-type defaulters to medical assistance or reducing insurance contribution burdens.

• Journal of The Korean Association For Science Education
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• v.32 no.3
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• pp.524-531
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• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.