• 한국경영과학회지
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• 제38권1호
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• pp.201-216
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• 2013

Diffusion is a basic mathematical tool for modern financial engineering. The theory of the estimation methods for diffusion models is an important topic of the financial engineering. Many researches have been tried to apply the likelihood estimation method for estimating diffusion models. However, the likelihood estimation method for diffusion is complicated and needs much amount of computing. In this paper we develop the estimation methods which are simple enough to be compared to the Euler approximation method, and efficient enough statistically to be compared to the likelihood estimation method. We devise pseudo-likelihood and propose the maximum pseudo-likelihood estimation methods. The pseudo-likelihoods are obtained by approximating the transition density with normal distributions. The means and the variances of the distributions are obtained from the delta expansion suggested by Lee, Song and Lee (2012). We compare the newly suggested estimators with other existing estimators by simulation study. From the simulation study we find the maximum pseudo-likelihood estimator has very similar properties with the maximum likelihood estimator. Also the maximum pseudo-likelihood estimator is easy to apply to general diffusion models, and can be obtained by simple numerical steps.

• Communications for Statistical Applications and Methods
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• 제27권1호
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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.

• 한국인터넷방송통신학회논문지
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• 제13권3호
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• pp.165-170
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• 2013

공간상에서 원하는 목표물의 도래 방향 추정은 수신 안테나에 입사하는 신호의 입사 방향을 찾는 것이다. 본 논문에서는 최대 우도 추정 방법을 이용하여 원하는 목표물의 도래 방향을 추정하였다. 도래 방향 추정방법은 최대 우도 방법에서 수신 신호 한계점 이상의 신호에 특이 값 분해를 적용하여 최대 우도 추정의 첨예도를 계산하여 원하는 목표물을 추정하였다. 모의실험을 통하여 본 연구에서 제안된 방법의 성능을 기존 방법과 비교분석하였다. 목표물 도래방향 추정에서 본 연구에서 제안한 방법이 고유치 전개를 하지 않기 때문에 처리시간 단축에서 효과적이고 원하는 목표물의 방향을 정확히 추정하였다. 본 연구에서 제안한 방법이 목표물 추정에서 기존 방법보다 우수함을 나타내었다.

• Journal of Information Technology Applications and Management
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• 제24권1호
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• pp.25-32
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• 2017

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

• 자동차안전학회지
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• 제13권4호
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• 한국음향학회지
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• 제13권4호
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• pp.20-31
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• 1994

다중 정현파의 주파수를 추정하는 최우도(ML) 방법은 주파수 추정에 정밀도를 보여주고 있으나, 최우도 함수가 주파수 추정에 쓰이는 경우 고도의 비선형성 때문에 추정에 많은 희생을 요구하고 있다. 본 논문에서는 최우도 방법의 비선형성을 개선하기 위해, 신호속에 포함된 정현 주파수의 추정을 용이하게 할 수 있는 단순화된 최우도 방법을 제시한다. 이 새로운 주파수 추정 방법을 백색 또는 칼라 잡음의 보기들에 적용하고, Monte-carlo 시뮬레이션을 실행하여 통계적 평균값, 평균 제곱근 및 상대 바이어스를 기존의 가장 우수한 방법인 MFBLP 방법과 비교한다. 또한 스펙트럼 파우어 밀도와 단위 원에서의 주파수 위치를 그림을 통하여 나타낸다.

• 대한전자공학회논문지
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• 제26권9호
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• pp.1436-1443
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• 1989

In this paper, we propose a maximum-likelihood estimation(MLE) method to obtain the location and the amplitude of the pulses in MPE( multi-pulse excitation)-LPC speech synthesis using multi-pulses as excitation source. This MLE method computes the value maximizing the likelihood function with respect to unknown parameters(amplitude and position of the pulses) for the observed data sequence. Thus in the case of overlapped pulses, the method is equivalent to Ozawa's crosscorrelation method, resulting in equal amount of computation and sound quality with the cross-correlation method. We show by computer simulation: the multi-pulses obtained by MLE method are(1) pseudo-periodic in pitch in the case of voicde sound, (2) the pulses are random for unvoiced sound, (3) the pulses change from random to periodic in the interval where the original speech signal changes from unvoiced to voiced. Short time power specta of original speech and syunthesized speech obtained by using multi-pulses as excitation source are quite similar to each other at the formants.

• 전기의세계
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• 제22권2호
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• pp.51-56
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• 1973

The comparative study of maximum likelihood estimation with Bayesian approach was made by statistical & computational methods in center of a priori information of static systems and the effect of a priori information on the accuracy of the estimatiion was also analyzed. Through the numerical computations of some examples by digital computer, we concluded that maximum likelihood method is better than Bayesian estimation except for almost certain a priori informations. The study may therefore contribute in identification problems of dynamical systems connected with a priori informations.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.263-272
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• 1996

임의의 기계에 대한 수명의 분포는 와이블분포를 하는 경우가 흔하다. 그리고 현실적으로 기계의 수명시간을 검정할 때, 시험시간및 여러 환경적인 제약에 의하여 표본으로 주어진 기계의 수명을 모두 관측하기는 어렵다. 그래서, 본 연구에서는 임의 중단모형 하에서 와이블분포의 모수를 최소제곱법(least squares method)을 이용하여 추정하고 기존의 최대우도추정량(maximum likelihood estimates)과 효율성의 측면에서 비교하고자 한다.

• International Journal of Reliability and Applications
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• 제9권1호
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• pp.31-52
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• 2008

In this paper generalized version of the geometric distribution is introduced. This distribution can be considered as a two-parameter generalization of the discrete geometric distribution. The main statistical and reliability properties of this distribution are discussed. Two methods of estimation, namely maximum likelihood method and the method of moments are used to estimate the parameters of this distribution. Simulation is utilized to calculate these estimates and to study some of their properties. Also, asymptotic confidence limits are established for the maximum likelihood estimates. Finally, the appropriateness of this new distribution for a set of real data, compared with the geometric distribution, is shown by using the likelihood ratio test and the Kolmogorove-Smirnove test.