• Proceedings of the Korean Association for Survey Research Conference
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• 2007.11a
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• pp.213-230
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• 2007

Although the response model has been frequently applied to nonresponse weighting adjustment or imputation, the estimation under callbacks has been relatively underdeveloped in the response model. The estimation method using the response probability is developed under callbacks. A replication method for the estimation of the variance of the proposed estimation is also developed. Since the true response probability is usually unknown, we study the estimation of the response probability. Finally, we propose an estimator under callbacks using the ratio imputation as well as the response probability. The simulation study illustrates our techniques.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.411-421
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• 2023

This paper provides a new estimation equation based on the concept of a minimum distance between the empirical and theoretical distribution functions under the most widely used progressive Type-II censoring scheme. For illustrative purposes, simulated and real datasets from a three-parameter Weibull distribution are analyzed. For comparison, the most popular estimation methods, the maximum likelihood and maximum product of spacings estimation methods, are developed together. In the analysis of simulated datasets, the excellence of the provided estimation method is demonstrated through the degree of the estimation failure of the likelihood-based method, and its validity is demonstrated through the mean squared errors and biases of the estimators obtained from the provided estimation equation. In the analysis of the real dataset, two types of goodness-of-fit tests are performed on whether the observed dataset has the three-parameter Weibull distribution under the progressive Type-II censoring scheme, through which the performance of the new estimation equation provided is examined.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.06a
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• pp.57-70
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• 2001

In this paper we consider a various estimation methods including the post-stratification estimation, regression estimation and calibration estimation or a generalized raking estimation under a unit nonresponse. All of them have a common type of calibration estimation based on the post-stratification for a categorical auxiliary variables.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.208-219
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• 2004

This paper considers a nonparametric estimation of lifetime distribution for ramp stress tests with stress bound under intermittent inspection. The test items are inspected only at specified time points an⊂1 so the collected observations are grouped data. Under the cumulative exposure model, two nonparametric estimation methods of estimating the lifetime distribution at use condition stress are proposed for the situation which the time transformation function relating stress to lifetime is a type of the inverse power law. Each of items is initially put on test under ramp stress and then survivors are put on test under constant stress, where all failures in the Inspection interval are assumed to occur at the midi)oint or the endpoint of that interval. Two proposed estimators of quantile from grouped data consisting of the number of items failed in each inspection interval are numerically compared with the maximum likelihood estimator(MLE) based on Weibull distribution.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.51-57
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• 2018

In this paper, we investigate the impact of channel estimation error on the D2D power control algorithm. In the previous work, D2D power control algorithm has been proposed under the assumption that the channel between the transmitter and the corresponding receiver is perfectly estimated. In reality, the channel estimation error is more often the case. The first question is that the power control algorithm designed for perfect channel estimation is still valid under the channel estimation error environment ? The second question is, if it is not valid, what could be the possible remedy for the channel estimation error ? In this paper, to answer the first question, we investigate the impact of the channel estimation error on the power control algorithm. We first review the D2D power control algorithm designed for perfect channel estimation. Then, we model the channel estimation error. Finally, we summarize the main results observed from the analysis of the simulation.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.185-191
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• 1997

Bayesian estimation of multinomial and Poisson parameters under starshped restriction is considered. Most Bayesian estimations in order restricted statistical inference require the high-dimensional integration which is very difficult to evaluate. Monte Carlo integration and Gibbs sampling are among alternative methods. The Bayesian estimation considered in this paper requires only evaluation of incomplete beta functions which are extensively tabulated.

• Journal of Sensor Science and Technology
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• v.29 no.6
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• pp.440-446
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• 2020

A well-known difficulty in attitude estimation based on inertial measurement unit (IMU) signals is the occurrence of external acceleration under dynamic motion conditions, as the acceleration significantly degrades the estimation accuracy. Lee et al. (2012) designed a Kalman filter (KF) that could effectively deal with the acceleration issue. Ahmed and Tahir (2017) modified this method by adjusting the acceleration-related covariance matrix because they considered covariance modeling as a pivotal factor in the estimation accuracy. This study investigates the effects of covariance modeling on estimation accuracy in an IMU-based attitude estimation KF. The method proposed by Ahmed and Tahir can be divided into two: one uses the covariance including only diagonal components and the other uses the covariance including both diagonal and off-diagonal components. This paper compares these three methods with respect to the motion condition and the window size, which is required for the methods by Ahmed and Tahir. Experimental results showed that the method proposed by Lee et al. performed the best among the three methods under relatively slow motion conditions, whereas the modified method using the diagonal covariance with a high window size performed the best under relatively fast motion conditions.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.289-298
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• 2003

We consider the estimation problem for the scale parameter of the Rayleigh distribution using weighted balanced loss function (WBLF) which reflects both goodness of fit and precision. Under WBLF, we obtain the optimal estimator which creates a kind of balance between Bayesian and non-Bayesian estimation. We also deal with the estimation of R ＝ P(Y < X) when Y and X are two independent but not identically distributed Rayleigh distribution under squared error loss function.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.42-46
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• 2012

A simple and accurate depth estimation algorithm for an IBVS (Image-Based Visual Servoing) is presented. Specifically, this algorithm is useful for under-actuated systems such as visual-guided quadrotor UAVs (Unmanned Aerial Vehicles). Since the image of a marker changes with changing pitch and roll angles of quadrotor, it is difficult to estimate depth. The proposed algorithm compensates a shape of the marker, so that the system acquire more accurate depth information without complicated processes. Also, the roll and pitch channels are decoupled so that the IBVS algorithm can be used in an under-actuated quadrotor system.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.419-427
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• 2020

In this study, a particle image velocimetry (PIV)-based pressure estimation method was investigated, with application to the wave-in-deck loading phenomenon. An experimental study was performed in a two-dimensional wave tank using a fixed deck structure under a focused wave, obtaining local pressures by pressure sensors, global loads by load cells, and instantaneous velocity fields using the PIV measurement technique. The PIV-based pressure estimation method was applied using the Euler equation as the governing equation, and the proper time step for the wave impact pressure was studied using the normalized root-mean-square deviation. The pressure estimation method showed good agreement for the local impact pressure in comparison with the measured pressure by the pressure sensors. However, some differences were observed in the peak pressure due to the limitations of the Euler equation and the sampling rate of the measurement system. Using the estimation method, the pressure fields during wave-in-deck loading were determined in the study, with an analysis of the mechanism of impact and negative pressure occurrence.