• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.403-414
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• 2009

Many researches have been devoted to the small area estimation related with the area level statistics. Almost all of the small area estimation methods are derived based on minimization of mean squared error(MSE). Recently Hwang and Shin (2008) suggested an alternative small area estimation method by minimizing mean squared percentage error. In this paper we apply this small area estimation method to the labor statistics, especially monthly wages by a branch area of labor department. The Monthly Labor Survey data (2007) is used for analysis and comparison of these methods.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.48.5-48
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• 2001

The resulting stabilizing controller in this paper consists of the disturbance estimator and the gain scheduled controller. The disturbance estimator tracks the unknown external disturbance and its derivative information in the closed-loop control system using fuzzy logic based adaptation law. Moreover, the gains of the stabilizing controller are appropriately scheduled according to the estimated values. Furthermore, since the estimation law is combined with the stabilizing controller in the closed control loop, it asymptotically minimizes the estimation error. In order to conrm the usefulness of the proposed control scheme, it is applied to the magnetic suspension systems.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.285-292
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• 1999

In this paper we propose the several estimators of the Lorenz curve in the Pareto distribution and obtain the bias and the mean squared error for each estimator. We compare the proposed estimators with the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE) in terms of the mean squared error (MSE) through Monte Carlo methods and discuss the results.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.661-668
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• 1999

Reliability estimation using Gibbs sampler considered for modeling mixture exponential reliability problems. Gibbs sampler is developed to compute the features of the posterior distribution. Bayesian estimation of complicated functions requires simpler esti-mation techniques due to the mathematical difficulties involved in the Bayes approach. The Maximum likelihood estimator and the Gibbs estimator of reliability of the system are derived. By simula-tion risk behaviors of derived estimators are compared. model de-termination based on relative error is considered. A numerical study with a simulated data set is provided.

• KIEE International Transactions on Power Engineering
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• v.2A no.3
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• pp.109-113
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• 2002

This paper addresses the issue of parameter error identification and estimation in electric power systems. Parameter error identification and estimation is carried out as a part of the state estimation. A two stage estimation procedure is used to detect and identify parameter errors. Suspected parameters are identified by the WLAV state estimator in the first stage. A new WLAV state estimator adding suspected system parameters in the state vector is used to estimate the exact values of parameters. Supporting examples are given by using the IEEE 14 bus system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.90-102
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• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• 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 Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.9
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• pp.451-458
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• 2000

This paper addresses the issues of the parameter error detection and identification in electric power systems. In this paper, the parameter error identification and estimation is carried out as part of the state estimation. A two stage estimation procedure is used to detect and identify the parameter errors. The suspected parameters are identified by the WLAV state estimator as the first stage. A new WLAV state estimator adding the suspected system parameters in the state vector is used to estimate the exact value of parameter errors. Supporting examples are given by using IEEE 14 bus system.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.1-18
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• 2000

In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.

• Structural Engineering and Mechanics
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• v.8 no.5
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• pp.513-529
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• 1999

A concept of hierarchical modeling, the newest modeling technology, has been introduced in early 1990's. This new technology has a great potential to advance the capabilities of current computational mechanics. A first step to implement this concept is to construct hierarchical models, a family of mathematical models sequentially connected by a key parameter of the problem under consideration and have different levels in modeling accuracy, and to investigate characteristics in their numerical simulation aspects. Among representative model problems to explore this concept are elastic structures such as beam-, arch-, plate- and shell-like structures because the mechanical behavior through the thickness can be approximated with sequential accuracy by varying the order of thickness polynomials in the displacement or stress fields. But, in the numerical, analysis of hierarchical models, two kinds of errors prevail, the modeling error and the numerical approximation error. To ensure numerical simulation quality, an accurate estimation of these two errors is definitely essential. Here, a local a posteriori error estimator for elastic structures with thin domain such as plate- and shell-like structures is derived using the element residuals and the flux balancing technique. This method guarantees upper bounds for the global error, and also provides accurate local error indicators for two types of errors, in the energy norm. Compared to the classical error estimators using the flux averaging technique, this shows considerably reliable and accurate effectivity indices. To illustrate the theoretical results and to verify the validity of the proposed error estimator, representative numerical examples are provided.