• Korean Journal of Food Science and Technology
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• v.25 no.2
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• pp.178-184
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• 1993

The procedure using equivalent time at reference temperature has been assessed for the estimation of kinetic parameters with experimental data. Kinetic studies of nonenzymatic browning reaction in model and food system were carried out with linearly increasing temperature method. These kinetic parameters, n, $k_{ref}$ and $E_a$ of the systems were evaluated from original data in one step by nonlinear least square regression. The one step procedure yielded efficiently accurate parameter estimation. Computer simulated data with the kinetic models were well consistent with experimental data (average correlation coefficient=0.96).

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.971-980
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• 2017

The varying coefficient regression model has gained lots of attention since it is capable to model dynamic changes of regression coefficients in many regression problems of science. In this paper we propose a varying coefficient regression model that effectively considers the errors on both input and response variables, which utilizes the kernel method in estimating the varying coefficient which is the unknown nonlinear function of smoothing variables. We provide a generalized cross validation method for choosing the hyper-parameters which affect the performance of the proposed model. The proposed method is evaluated through numerical studies.

• Journal of the Korean Society of Groundwater Environment
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• v.6 no.4
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• pp.180-187
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• 1999

Transmissivity is often estimated from specific capacity data because of the expense of conducting standard aquifer test to obtain transmissivity and the relative availability of specific capacity data. Most often, analytic expression relating specific capacity to transmissivity derived by Theis (1963). Brown (1963). and Logan (1964) are used in this analysis. The analytic solution typically used to predict transmissivity from specific capacity in alluvial aquifers assuming influence radius and/or storage coefficient of the aquifers. But those do not agree well with the measured transmissivity in fractured rock aquifers and in heterogeneous aquifers. Razack-Huntely (199l). Huntely-Steffey (1992). and Mace (1997) proposed emphirical rotations between specific capacity and transmissivity in heterogeneous alluvial aquifers. fractured rock aquifers, and karst aquifers. This study focuses on comparison between transmissivity and specific capacity data in volcanic rock aquifers of Jeju Island. Emphirical relation between the log of transmissivity and the log of specific capacity suggests they no linearly related (correlation coefficient 0.951) and the width of $\pm$0.25 log cycles in transmissivity includes 96.6% of data.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.363-369
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• 2003

The purpose of this study is to understand the dissipation pattern of excess pore pressure after liquefaction and to estimate the variation in permeability during shaking load, which should be known for settlement predictions of the ground undergoing liquefaction. In this study, 1-g shaking table tests were carried out for 5 different kinds of sands, all of which had high liquefaction potentials. During the tests excess pore pressure at various depths, and surface settlements were measured. The measured dissipation curve of the excess pore pressure after liquefaction was linearly simulated using the solidification theory, and from the analysis of the slopes of linearly simulated curves, the correlation between dissipation velocity and the gradation characteristics was obtained. By substituting this correlation and the measured settlement to the dissipation velocity equation recommended in solidification theory, the permeability during dissipation was calculated, which was used for estimating the permeability variation during shaking load. The dissipation velocity of excess pore pressure after liquefaction had a linear correlation with the effective grain size divided by the coefficient of uniformity. The permeability during dissipation and liquefaction increased by 1.1∼2.8 times and 1.4∼5 times compared to the initial permeability of the original ground, respectively. And the amount of increase became greater as the effective grain size of the test sand increased and the coefficient of uniformity decreased.

• Journal of the Korean Geotechnical Society
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• v.22 no.1
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• pp.35-44
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• 2006

In general typically granular soils contain a certain amount of fines. It is also widely recognized that foundation soils under working loads show highly non-linear behavior from very early stages of loading. In the present study, a series of laboratory tests with sands of different silt contents are conducted and methods to assess strength and stiffiness characteristics are proposed. Modified hyperbolic stress-strain model is used to analyze non-linearity of silty sands in terms of non-linear Degradation parameters f and g as a function of silt contents and Relative density Dr. Stress-strain curves were obtained from a series of triaxial tests on sands containing different amounts of silt. Initial shear modulus, which is used to normalize Degradation modulus of silty sands, was determined from resonant column test results. From the laboratory test results, it was observed that, as the Relative density increases, values of f decrease and those of g increase. In addition, it was found that values of f and g increase and decrease respectively as a Skeleton void ratio $(e_{sk})$ increases.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.767-783
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• 2013

Generalized Method of Moment(GMM) is a popular estimation method to estimate model parameters in empirical financial studies. GMM is frequently applied to estimate diffusion models that are basic techniques of modern financial engineering. However, recent research showed that GMM had poor properties to estimate the parameters that pertain to the diffusion coefficient in diffusion models. This research corrects the weakness of GMM and suggests alternatives to improve the statistical properties of GMM estimators. In this study, a simulation method is adopted to compare estimation methods. Out of compared alternatives, NGMM-Y, a version of improved GMM that adopts the NLL idea of Shoji and Ozaki (1998), showed the best properties. Especially NGMM-Y estimator is superior to other versions of GMM estimators for the estimation of diffusion coefficient parameters.

• Computational Structural Engineering
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• v.3 no.4
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• pp.10-13
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• 1990

구조물의 동적계수 추정방법과 이에 관련된 몇가지 사항들에 대해서 기술하였다. 여러 문제들중 가장 중요한 것이라고 생각되는 것으 소음에 대한 처리문제라고 여겨진다. 순차적 예측오차법과 같은 비선형 최적화기법을 사용하여 측정오차와 관련된 소음영향은 어느정도 처리되어질 수 있다고 생각되나, 많은 자유도를 가진 실제구조물의 거동을 간단한 모형식으로 나타냄으로써 야기되는 모형화 오차에 대해서는 아직 그 처리방법에 상당한 난점이 있다. 앞으로 이에 대한 많은 연구가 요구되는 실정이다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.898-902
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• 1999

In this paper, we proposed an algorithm for estimating parameters of nonlinear continuous-discrete state-space system. This algorithm uses the conventional extended Kalman filter(EKF) for estimating state variables, and modifies the recursive prediction error method for parameter estimation of the nonlinear system. Simulation results for both linear and nonlinear measurements under the environment of process and measurement noises show a convincing performance of the proposed algorithm.

• Journal of the Korea Society for Simulation
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• v.10 no.3
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• pp.31-46
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• 2001

Repeated measurements on units under different conditions are common in biological and biomedical studies. In a number of growth and pharmacokinetic studies, the relationship between the response and the covariates is assumed to be nonlinear in some unknown parameters and the form remains the same for all units. Nonlinear random coefficient models are used to analyze such repeated measurement data. Extended least squares methods are proposed in the literature for estimating the parameters of the model. However, neither objective function has closed form expression in practice. This paper proposes Monte Carlo methods to estimate the objective functions and the corresponding estimators. A simulation study that compare various methods is included.