• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.953-958
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• 2013

This study suggests a measurement error model based on skewed normal distribution instead of normal distribution to identify slope parameter properties in a simple liner regression model. We prove that the slope parameter in a simple linear regression model is underestimated.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.917-920
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• 1988

The effects of the errors due to incorrect a priori informations on the noise model as well as the system model in the continuous state model based optimal FIR filter is considered. When the optimal filter is perturbed, the error covariance is derived. From this equation, the performance of the state model based optimal FIR filter is analyzed for the given modeling error. Also the state model based optimal FIR filter is compared to the standard Kalman filter by an example.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.349-356
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• 2010

The ordinary least squares based estimator of the disturbance variance in a regression model for spatial panel data is shown to be asymptotically unbiased and weakly consistent in the context of SAR(1), SMA(1) and SARMA(1,1)-disturbances when there is measurement error in the regressor matrix.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.5
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• pp.97-102
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and the PI controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system in characterized by a nonlinear equation with coulomb friction. The PI controller regulates the error between the set-point and the system output which may be caused by modeling error, variations of parameters and disturbances. The output which may be caused by modeling error, variations of parameters and disturbances. The parameters of the model and the PI controller are adjusted offlinely by a genetic algorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.845-852
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• 2010

The outcomes of counts commonly occur in the area of disease mapping for mortality rates or disease rates. A Poisson distribution is usually assumed as a model of disease rates in conjunction with a gamma prior. The small area typically refers to a small geographical area or demographic group for which very little information is available from the sample surveys. Under this situation the model-based estimation is very popular, in which the auxiliary variables from various administrative sources are used. The empirical Bayes estimator under Poissongamma model has been considered with its accuracy measures. An accuracy measure using a bootstrap samples adjust the underestimation incurred by the posterior variance as an estimator of true mean squared error. We explain the suggested method through a practical dataset of hitters in baseball games. We also perform a Monte Carlo study to compare the accuracy measures of mean squared error.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.29 no.6
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• pp.520-526
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• 2007

The accuracy of model surgery is one of important factors which can influence the outcome of orthognathic surgery. To evaluate the accuracy of digitalized model surgery, we tried the model surgery on a software after transferring the mounted model block into a digital model, and compared the results with that of classical manual model surgery. We could get the following results, which can be used as good baseline analysis for the clinical application. 1. We made the 3D scanning of dental model blocks, and mounted on a software. And we performed the model surgery according to the previously arranged surgical plans, and let the rapid prototyping machine produce the surgical wafer. All through these process, we could confirm that the digital model surgery is feasible without difficulties. 2. The digital model surgery group (Group 2) showed a mean error of $0.0{\sim}0.1mm$ for moving the maxillary model block to the target position. And Group 1, which was done by manual model surgery, presented a mean error of $0.1{\sim}1.2mm$, which is definitely greater than those of Group 2. 3. Remounted maxillary model block with the wafers produced by digital model surgery from Group 2 showed the less mean error (0.2 to 0.4 mm) than that produced by manual model surgery in Group 1 (0.3 to 1.4 mm). From these results, we could confirm that the digital model surgery in Group 2 presented less error than manual model surgery of Group 1. And the model surgery by digital manipulation is expected to have less influence from the individual variation or degree of expertness. So the increased accuracy and enhanced manipulability will serve the digital model surgery as the good candidate for the improvement and replacement of the classical model surgery, if careful preparation works for the clinical adjustment is accompanied.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.124-131
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• 1997

This paper presents a modified adaptive control scheme that improves the transient performance of the overall system while maintaining the asymptotic convergence of the output error. The proposed control scheme is characterized as the added outer dynamic feedback loop on the conventional adaptive control scheme. This control scheme enables various robust control methods that were developed for standard model reference adaptive controllers to be applied to the proposed controller. In contrast with the modified adaptive controllers that use augmented errors to provide additional dynamic feedback, the proposed controller uses tracking error directly, thereby reducing the tracking error significantly in the transient state and making the error insensitive to noise.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4109-4115
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• 2014

Background: Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995 to 1999 were analyzed. Moreover, to estimate and assess the effect of demographic, diagnostic and clinical factors as well as medical and post-surgical variables on transition rates and the probability of misdiagnosis of relapse, a hidden Markov multi-state model was employed. Results: Classification errors of patients in alive state without a relapse ($e_{21}$) and with a relapse ($e_{12}$) were 0.22 (95% CI: 0.04-0.63) and 0.02 (95% CI: 0.00-0.09), respectively. Only variables of age and number of renewed treatments affected misdiagnosis of relapse. In addition, patient age and distant metastasis were among factors affecting the occurrence of relapse (state1${\rightarrow}$state2) while the number of renewed treatments and the type and extent of surgery had a significant effect on death hazard without relapse (state2${\rightarrow}$state3)and death hazard with relapse (state2${\rightarrow}$state3). Conclusions: A hidden Markov multi-state model provides the possibility of estimating classification error between different states of disease. Moreover, based on this model, factors affecting the probability of this error can be identified and researchers can be helped with understanding the mechanisms of classification error.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.315-324
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• 2002

For estimating the error variance under the relative squared error loss in two-way analysis of variance models, we provide a class of hierarchical Bayes estimators and then derive a subclass of the hierarchical Bayes estimators, each member of which dominates the best multiple of the error sum of squares which is known to be minimax. We also identify a subclass of non-minimax hierarchical Bayes estimators.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.28-28
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• 2000

Low-cost solid-state inertial sensors of three rate Gyroscopes and a triaxial Accelerometer are evaluated in static and dynamic environments. As a interim result, error models of each inertial sensors are generated. Model parameters with respect to temperature are acquired in static environment. These error models are included in an Extended Kalman Filter(EKF) to compensate bias error due to temperature variation. Experimental results in dynamic environment are included to show the validity of the each error model and the performance improvement of a compensated low cost inertial sensors for a navigational application