• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.85-90
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• 1997

Previous our numerical results in computing point kinetics equations show a possibility in developing approximations to estimate sensitivity responses of nuclear reactor We recalculate sensitivity responses by maintaining the corrections with first order of sensitivity parameter. We present a method for computing sensitivity responses of nuclear reactor based on an approximation derived from point kinetics equations. Exploiting this approximation, we found that the first order approximation works to estimate variations in the time to reach peak power because of their linear dependence on a sensitivity parameter, and that there are errors in estimating the peak power in the first order approximation for larger sensitivity parameters. To confirm legitimacy of our approximation, these approximate results are compared with exact results obtained from our previous numerical study.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.355-368
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• 2023

This article introduces the R package ELCIC (https://cran.r-project.org/web/packages/ELCIC/index.html), which provides an empirical likelihood-based information criterion (ELCIC) for model selection that includes, but is not limited to, variable selection. The empirical likelihood is a semi-parametric approach to draw statistical inference that does not require distribution assumptions for data generation. Therefore, ELCIC is more robust and versatile in the context of model selection compared to the currently existing information criteria. This paper illustrates several applications of ELCIC, including its use in generalized linear models, generalized estimating equations (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanisms of missing at random and dropout.

• Water Engineering Research
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• v.4 no.2
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• pp.69-85
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• 2003

Previously developed empirical equations used to calculate the parameters of the transient storage model are analyzed in depth in order to evaluate their behavior in representing solute transport in the natural streams with storage zone. A comparative analysis of the existing theoretical and experimental equations used to predict parameters of the transient storage (TS) model is reported. New simplified equations for predicting 4 key parameters of the TS model using hydraulic data sets that are easily obtained in the natural streams are also developed. The weighted one-step Huber method, which is one of the nonlinear multi-regression methods, is applied to derive new parameters equation. These equations are proven to be superior in explaining mixing characteristics of natural streams with the transient storage zone more precisely than the other existing equations.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.656-665
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• 2010

Shear modulus has been recognized as one of the important soil properties in dynamic analysis of ground and can be calculated from in situ measurement of shear wave velocity. Field seismic tests are the most accurate but expensive methods to investigate dynamic ground characteristics. Due to that reason, empirical equations for estimating the shear wave velocity are widely used rather than conducting in-situ tests. The most common equations are based on the N value obtained in conjuctions with a standard penetration test. In this paper, the field datas of standard penetration test and suspension PS logging measured in 126 sites of Korea were summarized and the correlation equations between N value and shear wave velocity are suggested.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.413-420
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• 2008

This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.219-225
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• 1998

The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.4
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• pp.232-239
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• 1988

The approximate overdetermined method is proposed for high resolution spectral estimation in case of short data record length or narrow band signal. And a new recursive AR parameter estimation is derived in the form of fast algorithm. For ARMA spectral estimation, two stage procedure is used in estimating ARMA parameters. First AR parameters are estimated by using the modified Yule-Walker equations, and then MA parameters are implicitly estimated by estimating numerator spectral(NS) coefficients.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.121-133
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• 2015

The TANK model has been widely used in rainfall-runoff modeling due to its simplicity of concept and computation while achieving forecast accuracy. A major barrier to the model application is to determine parameter values for ungauged watersheds, leading to the need of a method for the parameter estimation. The objective of this study was to develop regression equations for estimating the 3th TANK model parameters considering their variations for the ungauged watersheds. Thirty watersheds of dam sites and stream stations were selected for this study. A genetic algorithm was used to optimize TANK model parameters. Watershed characteristics used in this study include land use percent, watershed area, watershed length, and watershed average slope. Generalized equations were derived by correlating to the optimized parameters and the watershed characteristics. The results showed that the TANK model, with the parameters determined by the developed regression equations, performed reasonably with 0.60 to 0.85 of Nash-Sutcliffe efficiency for daily runoff. The developed regression equations for the TANK model can be applied for the runoff simulation particularly for the ungauged watersheds, which is common for upstream of agricultural reservoirs in Korea.

• Journal of Korean Society on Water Environment
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• v.26 no.6
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• pp.969-975
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• 2010

Estimating the organic matter loadings from individual treated sewage has become important for establishment of effective management strategies to control refractory organic matter (R-OM) in watersheds. For this study, regression equations were constructed using treated sewage data to convert the chemical oxygen demand (COD) concentrations, which are mostly available from open database, into total organic carbon (TOC) and R-OM concentrations. Effluent samples were collected from five major sewage treatment plants (STPs) located upstream of the lake Paldang. Variations in the OM concentrations were not associated with either the location of the STP or the sampling season. The effluent investigated were characterized by higher ratio of R-OM with respect to biodegradable organic matter (B-OM) and higher presence of dissolved organic matters (DOM) versus particulate organic matter (POM). Compared to $COD_{Mn}$, $COD_{Cr}$ exhibited higher oxidation efficiencies and greater variations in the concentrations. The concentrations of $COD_{Mn}$ were positively correlated with dissolved organic carbon (DOC), total organic carbon (TOC), and R-OM concentrations. There was nearly no seasonal and annual variation in the regression equations between $COD_{Mn}$ and TOC or R-OM concentrations. The constructed regression equations for TOC and R-OM were $0.650({\pm}0.071){\times}COD_{Mn}+1.426({\pm}0.575)$ and $0.340({\pm}0.083){\times}COD_{Mn}+2.054({\pm}0.670)$, respectively. The established equations are expected to contribute to estimating OM loadings from the STPs into the lake Paldang and also to compensating for the deficiency of the data for effluent OM concentrations in STP.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.665-674
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• 2008

Most of existing studies about the small area estimation deal with the estimation of parameters based on cross-sectional data. However, since many official statistics are repeatedly collected at a regular interval of time, for instance, monthly, quarterly, or yearly, we need an alternative model which can handle characteristics of these kinds of data. In this paper, we investigate the generalized estimating equation which can model time-dependency among response variables and is useful to analyze repeated measurement or longitudinal data. We compare with the generalized linear model and the generalized estimating equation through the estimation of unemployment rates of 25 areas in Gyeongsangnam-do and Ulsan. The data consist of the status of employment and some covariates from January to December 2005.