• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.749-756
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• 2012

An approach widely used for small area estimation is based on linear mixed models. However, when the functional form of the relationship between the response and the input variables is not linear, it may lead to biased estimators of the small area parameters. In this paper we propose M-quantile kernel regression for small area mean estimation allowing nonlinearities in the relationship between the response and the input variables. Numerical studies are presented that show the sample properties of the proposed estimation method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.2
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• pp.112-121
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• 2013

The calibration of resistance factor in reliability theory for limit state design of gravel compaction piles (GCP) requires a reliable estimate of ultimate bearing capacity. The static load test is commonly used in geotechnical engineering practice to predict the ultimate bearing capacity. Many graphical methods are specified in the design standard to define the ultimate bearing capacity based on the load-settlement curve. However, it has some disadvantages to ensure reliability to obtain an uniform ultimate load depend on engineering judgement. In this study, a well-fitting nonlinear regression model is proposed to estimate the ultimate bearing capacity, for which a nonlinear regression analysis is applied to estimate the ultimate bearing capacity of GCP and the results are compared with those calculated using previous graphical method. Affect the resistance factor of the estimate method were analyzed. To provide a database in the development of limit state design, the load test conditions for predicting the ultimate bearing capacity from static load test are examined.

• Journal of the Society of Disaster Information
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• v.12 no.1
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• pp.74-88
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• 2016

Predicting and estimating the disaster characteristics are very important for disaster planning such as prevention, preparedness, response, and recovery. Especially, if we can predict the flood damage before flooding, the predicted or estimated damage will be a very good information to the decision maker for the response and recovery. However, most of the researches, have been performed for calculating disaster damages only after disasters had already happened and there are few studies that are related to the prediction of the damages before disaster. Therefore, the objective of this study was to predict and estimate the flood damages rapidly considering the damage scale and effect before the flood disaster, For this the relationship of rainfall and damage had been suggested using nonlinear regression equation so that it is able to predict the damages according to rainfall. We compared the estimated damages and the actual ones. As a result, the damages were underestimated in 14.16% for Suwon-city and 15.81% for Yangpyeong-town but the damage was overestimated in 37.33% for Icheon-city. The underestimated and overestimated results could be occurred due to the uncertainties involved in natural phenomenon and no considerations of the 4 disaster steps such as prevention, preparedness, response, and recovery which were already performed.. Therefore, we may need the continuous study in this area for reducing various uncertainties and considering various factors related to disasters.

• The Journal of Engineering Geology
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• v.18 no.4
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• pp.359-369
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• 2008

For the design and effective management of underground storage caverns, preliminary investigation on the hydrogeologic parameters around caverns and analysis on the groundwater flow must be carried out. The data collection is very imporatnat task for the hydrogeologic design so various hydraulic tests have been performed. When analyzing the injection/fall off test data, existing graphical method to estimate the parameters in Theis' equation is widely used. However this method has some sources of error when estimating parameters by means of human faults. Therefore the method of estimating parameters by means of statistical methods such as regression type is evaluated as a useful tool. In this study, nonlinear regression analysis for the Theis' equation is suggested and applied to the estimation of parameters for the real field interference data around underground storage caverns. Damping parameter which reduce the iteration numbers and inhance the convergence is also introduced.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.595-611
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• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.257-266
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• 2016

Random Coefficient Autoregressive models (RCA) have attracted increased interest due to the wide range of applications in biology, economics, meteorology and finance. We consider an RCA as an appropriate model for non-linear properties and better than an AR model for linear properties. We study the methods of RCA parameter estimation. Especially we proposed the special case that an random coefficient ${\phi}(t)$ has the initial value ${\phi}(0)$ in the RCA model. In practical study, we estimated the parameters and compared Prediction Error Sum of Squares (PRESS) criterion between AR and RCA using Korean Mumps data.

• Environmental and Resource Economics Review
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• v.15 no.3
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• pp.405-423
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• 2006

This paper develops theoretical model between economic growth and pollution as follows: First, emissions are generated from final good production process and technology accumulation. Second, pollution is directly connected with increase in final good production or in consumption, Third, no pollution abatement activity would be undertaken. Fourth, reproducible factors associated with labor and capital input are used in production function. We also test the existence of nonlinear Dynamics between economic growth and pollution using an exponential smooth transition autoregressive model(ESTAR). We find the presence of nonlinear dynamics between economic growth and pollution with a time series data for Seoul. This result shows indirectly that an inverted U relationship between air pollution and economic growth exists.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.569-578
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• 2020

By estimating conditional quantile functions of the response, quantile regression (QR) can provide comprehensive information of the relationship between the response and the predictors. In addition, kernel quantile regression (KQR) estimates a nonlinear conditional quantile function in reproducing kernel Hilbert spaces generated by a positive definite kernel function. However, it is infeasible to use the KQR in analysing a massive data due to the limitations of computer primary memory. We propose a divide and conquer based KQR (DC-KQR) method to overcome such a limitation. The proposed DC-KQR divides the entire data into a few subsets, then applies the KQR onto each subsets and derives a final estimator by aggregating all results from subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.327-344
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• 2007

Ridge partial least squares regression (RPLS) is a regression method which can be obtained by combining ridge regression and partial least squares regression and is intended to provide better predictive ability and less sensitive to overfitting. In this paper, explicit expressions for the shrinkage factor of RPLS are developed. The structure of the shrinkage factor is explored and compared with those of other biased regression methods, such as ridge regression, principal component regression, ridge principal component regression, and partial least squares regression using a near infrared data set.