• Journal of the Korean Statistical Society
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• v.12 no.2
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• pp.81-90
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• 1983

The least squares estimator of disturbance variance in a regression model is biased under a serial correlation. Under the assumption of an AR(I), Theil(1971) crudely related the bias with the autocorrelation of the disturbances and the autocorrelation of the explanatory variable for a simple regression. In this paper we derive a relation which relates the bias with the autocorrelation of disturbances and the autocorrelation of explanatory variables for a multiple regression with improved precision.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.151-158
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• 2000

This dissertation measures transitory items using earnings change scaled by beginning-of-period price(FreemanㆍTse 1992) and the earnings-to-price ratio(AliㆍZarowin 1992). Contextual regression model results confirm the incremental explanatory power for predominantly permanent earnings, and suggest that cash flows also have incremental explanatory power in the presence of predominantly permanent earnings. But contextual regression results represent that while earnings are consistent with a smaller marginal impact from extreme (transitory) earnings on abnormal returns, cash flows have no greater impact on abnormal returns in the presence of large transitory components in earnings.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.585-595
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• 2001

A second-order response surface model is often used to approximate the relationship between a response factor and a set of explanatory factors. In this article, we deal with canonical analysis in response surface models. For the interpretation of the geometry of second-order response surface model, standard errors and confidence intervals for the eigenvalues of the second-order coefficient matrix play an important role. If the confidence interval for some eigenvalue includes 0 or the estimate of some eigenvalue is very small (near to 0) with respect to other eigenvalues, then we are able to delete the corresponding canonical factor. We propose a formulation of criterion which can be used to select canonical factors. This criterion is based on the IMSE(=Integrated Mean Squared Error). As a result of this method, we may approximately write the canonical factors as a set of some important explanatory factors.

• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.539-542
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• 2006

This study confirmed econometrically the causality of forest inventory and roundwood supply using Korean data. In general, forest inventory is included as explanatory variable in roundwood supply function. We checked whether each series is stationary or not before using it in the model, and determined whether the combination of the series is comtegrated. The relationship between forest inventory and roundwood supply was represented by bivariate vector autoregressive model. The causality of forest evidence of the causal relationship between change in forest inventory and change in roundwood supply in Korea. That is, change in forest inventory does not cause change in roundwood supply in Korea. It seems reasonable not to include forest inventory as explanatory variable in roundwood supply function in Korea.

• Journal of Families and Better Life
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• v.33 no.4
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• pp.51-65
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• 2015

Case management is becoming increasingly more important in the field of social welfare. The purpose of this study was to examine the practice of case management at community child centers and to identify factors influencing their performance. In addition, this study also analysed the performance level of case management at each stage and the factors influencing the level at each stage. For the purpose of this study, data obtained from about 181 community child center workers in Busan, Ulsan and Gyeongsangnamdo were analyzed. According to the results of the analysis, in Model 1 which represents the category of socio-demographic and personal factors, three factors namely gender, experience in case management, and interpersonal skills were found to be significantly influential and they have an explanatory power of 21.5%. Under the category of organizational factors in Model 2, four factors were found to be influential: experience in case management, interpersonal skills, availability of case management guidelines, and holding case conferences. These factors account for 33% explanatory power, 11.5% higher than that of Model 1. In Model 3 which represents the third category of community network literacy, three factors namely interpersonal skills, availability of case management guidelines, and ability to utilize networks were found to influence case management at community child centers, and they have an explanatory power of 43.4%, 10.4% higher than that of Model 2. For the practice of social welfare, these findings have the implication that community child center workers need to improve their interpersonal skills by cultivating communication skills, collaborative problem-solving skills, conflict-management skills, and other relevant skills. Furthermore, it is necessary to provide specific guidelines for case management, to have regular case conferences, to establish a community network, and to reinforce cooperation and mutual support among institutions within the network.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.279-289
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• 2010

In this study, we propose a simple statistical model for determining the admission or discharge of 668 patients with a chief complaint of dyspnea. For this, we use 11 explanatory variables which are chosen to be important by clinical experts among 55 variables. As a modification process, we determine the discharge interval of each variable by the kernel density functions of the admitted and discharged patients. We then choose the optimal model for determining the discharge of patients based on the number of explanatory variables belonging to the corresponding discharge intervals. Since the numbers of the admitted and discharged patients are not balanced, we use, as the criteria for selecting the optimal model, the arithmetic mean of sensitivity and specificity and the harmonic mean of sensitivity and precision. The selected optimal model predicts the discharge if 7 or more explanatory variables belong to the corresponding discharge intervals.

• Journal of Korean Academy of Nursing
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• v.52 no.4
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• pp.407-420
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• 2022

Purpose: This structural model study was constructed and verified a hypothetical model to examine the effects of parenting stress, social resources, family resources, and positive coping on resilience among mothers of children with developmental disabilities. Methods: Data were collected using self-report structured questionnaires, from October 19 to October 30, 2018, with 214 mothers caring for children with developmental disabilities under the age of 20 years. Results: In the fitness test results of the hypothesis model, with the fit index 𝛘² (p) = 69.27 (< .001), and the normed fit indices (𝛘² = 1.87, GFI = .94, CFI = .97, NFI = .93, and TLI = .95, RMSEA = .06, SRMR = .06), this study satisfies the good fitness in standards. There are seven statistically significant paths among the 10 paths set in the hypothetical model. The explanatory power of parenting stress and social resources, which affects the family resources was 41.4%, the explanatory power of parenting stress, social resources, and family resources affecting the positive coping was 58.9%, and the explanatory power of parenting stress, social resources, family resources, and positive coping affecting resilience was 55.5%. Conclusion: Positive coping, family resources, and social resources of mothers of children with developmental disabilities directly affect their resilience, and parenting stress indirectly affects it. Therefore, to improve the resilience of mothers of children with developmental disabilities, it is necessary to develop a systematic nursing intervention that considers parenting stress, social resources, family resources, and positive coping.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.801-808
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• 2018

The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.5
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.366-370
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• 2013

Regression Analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper introduce Theil's method to find a fuzzy regression model which explain the relationship between explanatory variable and response variables. Theil's method is a robust method which is not sensive to outliers. Theil's method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. We propose an example to show Theil's estimator is robust than the Least squares estimator.