• Journal of Korean Academy of Nursing
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• v.43 no.2
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• pp.154-164
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• 2013

Purpose: The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature. Methods: Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models. Results: Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis. Conclusion: Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.193-204
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• 1998

It is quite that we have to estimate the derivative of the regression function. The Bezier curve, rarely known to statisticians, is very popular in computer graphics area. In this paper, we use nonparametric method via the Bezier curve, and apply this method to real data set. This method seems to be very easy to compute and can be easily applied to other smoothing techniques.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.253-262
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• 2016

A Rating curve is a regression equation of discharge versus stage for a given point on a stream where the stream discharge is measured across the stream channel with a stage and discharge measurement. The curve is generally used to calculate discharge based on the stage. However, the existing approach showed problems in terms of estimating uncertainty associated with regression parameters including the separation parameter for low and high flow. In this regard, this study aimed to develop a new method for the aforementioned problems based on Bayesian approach, which can better estimate the parameter and its uncertainty. In addition, this study used a Bayesian Multi-Segmented (Bayesian M-S) model which is provided a comparison between the existing and proposed scheme. The proposed model showed better results for the parameter estimation than the existing approach, and provided better performance in terms of estimating uncertainty range.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.759-767
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• 2017

Two main parameters of NRCS-CN method are curve numbers and intial loss ratio. They are generally selected according to the guideline of US National Engineering Handbook, however, they might cause errors on estimated runoff in Korea because there are differences between soil types and hydrological characteristics of Korean watersheds and those of United States. In this study, applying asymptotic CN regression method, we suggested eight modified NRCS-CN models to decide optimum runoff estimation model for Korean watersheds. RSR (RMSE-observations standard deviation ratio) and NSE (Nash-Sutcliffe efficiency) were used to evaluate model performance, consequently M6 for gauged basins (Avg. RSR was 0.76, Avg. NSE was 0.39) and M7 for ungauged basins (Avg. RSR was 0.82, Avg. NSE was 0.31) were selected. Furthermore it was observed that initial loss ratios ranging from 0.01 to 0.10 were more adequate than the fixed ${\lambda}=0.20$ in most of basins.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.943-958
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• 2008

This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.103-110
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• 2005

Software Development is converting from structural to object oriented method. The later software development prefers the iterative process applications, not aterfall process and based on use case model, the requirements are expressed and based on this, analysis, design and coding are accomplished. Therefore, size of the software to be developed is estimated basing on use case and it is only possible to maintain the project success by estimating development effort, cost and development period. Even though development effort estimation models related current use case point. there is no appropriate development effort estimating. This paper shows, as a result of applying the development effort estimating model about UCP to the growth curve, a superior performance improvement to current statistical models. Therefore, estimation of development effort by applying this model, project development maintenance can be appropriately carried out.

• Journal of the Korean Geosynthetics Society
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• v.15 no.2
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• pp.65-75
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• 2016

In this study, we constructed a database by collecting field pullout test data of the soil nailing using pressurized grouting, and suggested a method to estimate the ultimate pullout resistance using nonlinear regression analysis to overcome the problems of ultimate pullout resistance estimation using graphical methods. The load-displacement curve estimated by nonlinear regression showed a very high correlation with the field pullout test data. Estimated ultimate pullout load by nonlinear regression method was average 29% higher than estimated ultimate pullout load using previous graphical method. A sigmoidal growth model was found to be the best-fitting nonlinear regression model against rapid pullout failure. Further, an asymptotic regression model was found to be the best fit against progressive nail pullout. The unit ultimate skin friction suggested in this research reflected in the domestic geotechnical characteristics and the specifications of the pressurized grouting method. This research is expected to contribute towards establishing an independent design standard for the soil nailing by providing solutions to the problems that occur when using design charts based on foreign research.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.363-374
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• 2008

Intensity-Duration-Frequency (IDF) curve that is essential to calculate rainfall quantiles for designing hydraulic structures in Korea is generally formulated by regression analysis. In this study, IDF curve derived by the cumulative distribution function ("IDF by CDF") of the proper probability distribution function (PDF) of each site is suggested, and the corresponding parameters of IDF curve are computed using genetic algorithm (GA). For this purpose, IDF by CDF and the conventional IDF derived by regression analysis ("IDF by REG") were computed for 22 Korea Meteorological Administration (KMA) rainfall recording sites. Comparisons of RMSE (root mean squared error) and RRMSE (Relative RMSE) of rainfall intensities computed from IDF by CDF and IDF by REG show that IDF by CDF is more accurate than IDF by REG. In order to accommodate the effect of the recent intensive rainfall of Korea, the rainfall intensities computed by the two IDF curves are compared with that by at-site frequency analysis using the rainfall data recorded by 2006, and the result from IDF by CDF show the better performance than that from IDF by REG. As a result, it can be said that the suggested IDF by CDF curve would be the more efficient IDF curve than that computed by regression analysis and could be applied for Korean rainfall data.