• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.43-46
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• 1995

Adaptive algorithms based on gradient adaptation have been extensively investigated and successfully jointed with active noise/vibration control applications. The Filtered-X LMS algorithm became one of the basic feedforward algorithms in such applications, but still is not fully understood. The error path model effect on the Filtered-X LMS algorithm has been under the investigation and some useful properties related stability has been discovered. We are interested in utilizing the fact that the model error caused by the way optimizing the error path model in a view point of convergence speed of Filtered-X LMS algorithm for pure tone noise suppression application without any performance loss at steady state.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.667-676
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• 1999

We investigate the effects of omission of initial observations in each individuals in the panel data regression model when the disturbances follow a serially correlated one way error components. We show that the first transformed observation can have a relative large hat matrix diagonal component and a large influence on parameter estimates when the correlation coefficient is large in absolute value.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.77-86
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• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.253-263
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• 2021

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.507-518
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• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.72-82
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• 2023

The EOTS(Electro Optical Tracking System) must have stabilization performance to provide high-quality images under disturbance environment. In this paper, we present a controller that can minimize the LOS error and has a simple structure. Hence, to evaluate the performance of this controller, analysis in the frequency domain and LOS error measurement are performed. In order to measure the LOS error without a 'rate table' that requires a lot of facility investment, we propose a design method for disturbance model that considers the operating environment of the EOTS. Finally, the performance of the stabilization algorithm is evaluated by the proposed disturbance model.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.147-150
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• 2010

Ionopheric deley is the largest error sources in GNSS positining. The single frequency receiver user needs an ionospheric model like the Klobuchar model or NeQuick model to eliminate the ionospheric error. In this study we estimated VTEC(Vertical Total Electron Content) over DAEJ station using the two models in each season. We compared the results with Global Ionosphere Maps and International Reference Ionosphere model predictions. As a result, the NeQuick model was more accurate than Klobuchar model.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.4
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• pp.262-271
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• 2011

In this work, we present a unified analytical surface potential model, valid for both PD and FD SOI MOSFETs. Our model is based on a simplified one dimensional and purely analytical approach, and builds upon an existing model, proposed by Yu et al. [4], which is one of the most recent compact analytical surface potential models for SOI MOSFETs available in the literature, to improve its accuracy and remove its inconsistencies, thereby adding to its robustness. The model given by Yu et al. [4] fails entirely in modeling the variation of the front surface potential with respect to the changes in the substrate voltage, which has been corrected in our modified model. Also, [4] produces self-inconsistent results due to misinterpretation of the operating mode of an SOI device. The source of this error has been traced in our work and a criterion has been postulated so as to avoid any such error in future. Additionally, a completely new expression relating the front and back surface potentials of an FD SOI film has been proposed in our model, which unlike other models in the literature, takes into account for the first time in analytical one dimensional modeling of SOI MOSFETs, the contribution of the increasing inversion charge concentration in the silicon film, with increasing gate voltage, in the strong inversion region. With this refinement, the maximum percent error of our model in the prediction of the back surface potential of the SOI film amounts to only 3.8% as compared to an error of about 10% produced by the model of Yu et al. [4], both with respect to MEDICI simulation results.

• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.978-993
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• 2013

It can be advantage as well as disadvantage to use the spatial weight matrix in a spatial regression model; it would benefit from explicitly quantifying spatial relationships between geographical units, but necessarily involve subjective judgment while specifying the matrix. We took Incheon City as a study area and investigated how the fitness of a spatial regression model changed by constructing various spatial weight matrices. In addition, we explored neighborhood segmentation in the study area and analyzed any influence of it on the model adequacy of two basic spatial regression models, i.e., spatial lagged and spatial error models. The results showed that it can help to improve the adequacy of models to specify the spatial weight matrix strictly, that is, interpreting the neighborhood as small as possible when estimating land price. It was also found that the spatial error model would be preferred in the area with serious spatial heterogeneity. In such area, we found that its spatial heterogeneity can be alleviated by delineating sub-neighborhoods, and as a result, the spatial lagged model would be preferred over the spatial error model.