• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.97-108
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• 2020

This paper compares several methods for estimating parameters of normal inverse Gaussian distribution. Ordinary maximum likelihood estimation and the method of moment estimation often do not work properly due to restrictions on parameters. We examine the performance of adjusted estimation methods along with the ordinary maximum likelihood estimation and the method of moment estimation by simulation and real data application. We also see the effect of the initial value in estimation methods. The simulation results show that the ordinary maximum likelihood estimator is significantly affected by the initial value; in addition, the adjusted estimators have smaller root mean square error than ordinary estimators as well as less impact on the initial value. With real datasets, we obtain similar results to what we see in simulation studies. Based on the results of simulation and real data application, we suggest using adjusted maximum likelihood estimates with adjusted method of moment estimates as initial values to estimate the parameters of normal inverse Gaussian distribution.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.128-136
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• 2008

In this paper, we propose a variable step search fast motion estimation algorithm using local statistics of neighboring motion vectors. Using the degree of correlation between neighboring motion vectors, motion search range is adaptively adjusted to reduce unnecessary search points. Based on the adjusted search range, motion vector is obtained by variable search step. Experimental results show that the proposed algorithm has the capability to dramatically reduce the search points and computing cost for motion estimation, comparing to fast full spiral search motion estimation and other fast motion estimation.

• Research in Community and Public Health Nursing
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• v.14 no.4
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• pp.707-716
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• 2003

Purpose: This study was to examine estimation variables and predicted probability for school-aged children's school adaptation. Methods: The subjects of this study consisted of 614 children. Data were analyzed by logistic regression using SPSS Windows 11.0. Results: 1. Girls adjusted themselves 1.65 times easier than boys, and 5th-and 6th-graders adjusted themselves slightly easier than 4th-graders. Children with monthly pocket money lower than 10,000 won adjust adjusted 2.92 times easier than those with a larger amount of pocket money, unhealthy children adjusted themselves 2.44 times easier than healthy ones, and children with low academic achievement adjusted themselves 2.68 times easier than those with high academic achievements. 2. As for family economic status, children who thought the economic condition of their family poor adjusted themselves 2.41 times easier than those who thought the economic condition of their family fine. As for conversation with people, children conversing with families and relatives adjusted themselves 2.69 times easier and those conversing with friends and others adjusted themselves 2.71 times easier than those conversing with none. Children from normal families adjusted themselves 3.44 times easier than those from deficiency families. 4. The overall prediction rate was 73.9%. Conclusions: It is concluded that nurses, teachers and parents are important in improving school-aged children's adaptation to school life.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.131-142
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• 1999

Estimators for domains and approximate estimators of their variance are derived using post-stratified complex sample. Furthermore we propose an adjusted variance estimator of a domain mean in case of considering the post-stratified complex sample as simple random sample. A simulation study based on the data of Farm Household Economy Survey is presented to compare variance estimators numerically. From the study we showed that our adjusted variance estimator compensate for the under-estimation problem considerably.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.281-290
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• 2007

Linear estimator, a weighted sum of the sample observation, is commonly adopted to estimate the finite population parameters such as population totals in survey sampling. The weight for a sampled unit is often constructed by multiplying the base weight, which is the inverse of the first-order inclusion probability, by an adjustment term that takes into account of the auxiliary information obtained throughout the population. The linear estimator using the weight adjustment is often more efficient than the one using only the bare weight, but its valiance estimation is more complicated. We discuss variance estimation for a general class of weight-adjusted estimator. By identifying that the weight-adjusted estimator can be viewed as a function of estimated nuisance parameters, where the nuisance parameters were used to incorporate the auxiliary information, we derive a linearization of the weight-adjusted estimator using a Taylor expansion. The method proposed here is quite general and can be applied to wide class of the weight-adjusted estimators. Some examples and results from a simulation study are presented.

• Journal of Power System Engineering
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• v.7 no.1
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• pp.74-81
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• 2003

A system identification is to estimate the mathematical model on the base of input output data and to measure the output in the presence of adequate input for the controlled system. In the traditional system control field, most identification problems have been thought as estimating the unknown modeling parameters on the assumption that the model structures are fixed. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input output case with the observed noise. We suggest the adjusted least squares method as a consistent estimation method in the system identification in the case where there is observed noise only in the output. In this paper the adjusted least squares method has been developed from the least squares method and the efficiency of the estimating results was confirmed by the generating data with the computer simulations.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.655-664
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• 2015

A nonresponse adjusted raking ratio estimator that consists of weighting adjustment using estimated response probability and raking procedure is often used to reduce the nonresponse bias and keep the calibration property of the estimator. We investigated asymptotic properties of nonresponse adjusted raking ratio estimator and proposed a variance estimator. A simulation study is used to examine the performance of suggested estimators.

• Journal of Korean Society of Steel Construction
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• v.25 no.4
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• pp.369-377
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• 2013

The age-adjusted effective modulus method has been known to provide more precise assessment than the traditional Yassumi method for long-term behavior estimation of prestressed composite girders. The age-adjusted effective modulus method, however, involves complicated calculation, thereby making the Yassumi method more prevalent in actual design. This study presents rational approaches to revise creep coefficients for the Yassumi method by using parametric study results obtained from the age-adjusted effective modulus method.

• The Journal of the Acoustical Society of Korea
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• v.37 no.3
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• pp.156-162
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• 2018

The LMF (Least Mean Fourth) algorithm is well known for its fast convergence and low steady-state error especially in non-Gaussian noise environments. Recently, there has been increasing interest in the LMS (Least Mean Square) algorithms with self-adjusted step size. It is because the self-adjusted step-size LMS algorithms have shown to outperform the conventional fixed step-size LMS in the various situations. In this paper, a self-adjusted step-size LMF algorithm is proposed, which adopts an averaged gradient based step size as a self-adjusted step size. It is expected that the proposed algorithm also outperforms the conventional fixed step-size LMF. The superiority of the proposed algorithm is confirmed by the simulations in the time invariant and time variant channels.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.41-43
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• 2004

This paper is concerned with attitude estimation using low cost, small-sized accelerometers and gyroscopes. A two step extended Kalman filter is proposed, which adaptively compensates external acceleration. External acceleration is the main source of estimation error. In the proposed filter, direction of external acceleration is estimated. According to the estimated direction, the accelerometer measurement covariance matrix of the two step extended Kalman filter is adjusted. The proposed algorithm is verified through experiments.