• East Asian Economic Review
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• 제24권1호
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• pp.61-87
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• 2020

Five diffusion models are estimated using three different foreign exchange rates to find an appropriate model for each. Daily spot exchange rates expressed as the prices of 1 euro, 1 British pound and 100 Japanese yen in US dollars, respectively denoted by USD/EUR, USD/GBP, and USD/100JPY, are used. The maximum likelihood estimation method is implemented after deriving an approximate log-transition density function (log-TDF) of the diffusion processes because the true log-TDF is unknown. Of the five models, the most general model is the best fit for the USD/GBP, and USD/100JPY exchange rates, but it is not the case for the case of USD/EUR. Although we could not find any evidence of the mean-reverting property for the USD/EUR exchange rate, the USD/GBP, and USD/100JPY exchange rates show the mean-reversion behavior. Interestingly, the volatility function of the USD/EUR exchange rate is increasing in the exchange rate while the volatility functions of the USD/GBP and USD/100Yen exchange rates have a U-shape. Our results reveal that more care has to be taken when determining a diffusion model for the exchange rate. The results also imply that we may have to use a more general diffusion model than those proposed in the literature when developing economic theories for the behavior of the exchange rate and pricing foreign currency options or derivatives.

• 대한공간정보학회지
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• 제8권2호
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• pp.47-60
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• 2000

본 연구에서는 인공위성영상을 이용한 토지피복 분류방법 중 파라메트릭한 분류와 비-파라메트릭한 분류의 대표성을 띤 최대우도 분류법과 신경망을 이용한 분류방법을 사용하여 분류정확도를 비교하였다. 분류정확도의 평가에 있어서 일반적인 분석가들이 사용하는 훈련지역에 대한 분류 정확도의 분석뿐만 아니라, 시험지역에 대한 정확도분석을 하였다. 그 결과, 최대우도분류기에 비하여 신경망의 분류기가 일반적인 훈련데이터의 분류에 있어서 약 3% 우월하였으며, 지상검증데이터를 사용한 분류결과에서는 시험에 사용된 두 분류기 모두 빈약한 분류결과를 나타내었으나, 신경망에 의한 분류가 최대우도에 비하여 약 10%정도 보다 신뢰할 수 있는 결과를 얻을 수 있었다.

• The Journal of Asian Finance, Economics and Business
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• 제5권1호
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• pp.11-16
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• 2018

This research examines the alternative ways of estimating the coefficient of non-diversifiable risk, namely beta coefficient, in Capital Asset Pricing Model (CAPM) introduced by Sharpe (1964) that is an essential element of assessing the value of diverse assets. The non-parametric methods used in this research are the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator). The Jackknife, the resampling technique, is also employed to validate the results. According to finance literature and common practices, these coecients have often been estimated using Ordinary Least Square (LS) regression method and monthly return data set. The empirical results of this research pointed out that the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) performed much better than Ordinary Least Square (LS) in terms of eciency for large-cap stocks trading actively in the United States markets. Interestingly, the empirical results also showed that daily return data would give more accurate estimation than monthly return data in both Ordinary Least Square (LS) and robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) regressions.

• Communications for Statistical Applications and Methods
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• 제18권5호
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• pp.625-636
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• 2011

본 논문에서는 로그정규분포의 엔트로피에 대한 모수적 추정량으로 최소분산비편향추정량과 최대가능도추정량을 제시하고 성질을 비교한다. 각 추정량의 분산을 유도해서 일치성을 밝히고 최대가능도 추정량의 편향이 추정에 미치는 영향을 분석한다. 델타근사방법을 이용해서 얻은 추정량의 분포를 제시하고 적합도 평가를 통한 유도한 분포의 확증을 위해서 모의실험을 수행한다. 평균제곱오차에 의한 상대적 효율성에 대한 조사를 통해 두 추정량의 성능을 비교한다. 모의실험의 결과에서 최소분산비편향추정량은 최대가능도 추정량보다 더 좋은 효율을 보이는 것으로 나타나며, 특히 표본크기와 분산이 동시에 작아짐에 따라 효율이 점점 높아지게 되어 월등히 나은 성능을 발휘함을 볼 수 있다.

• Communications for Statistical Applications and Methods
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• 제19권5호
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• pp.697-704
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• 2012

The maximum likelihood(ML) estimation of the scale parameters of an exponential distribution based on progressive Type II censored samples is given. The sample is multiply censored (some middle observations being censored); however, the ML method does not admit explicit solutions. In this paper, we propose multiply progressive Type II censoring. This paper presents the statistical inference on the scale parameter for the exponential distribution when samples are multiply progressive Type II censoring. The scale parameter is estimated by approximate ML methods that use two different Taylor series expansion types ($AMLE_I$, $AMLE_{II}$). We also obtain the maximum likelihood estimator(MLE) of the scale parameter under the proposed multiply progressive Type II censored samples. We compare the estimators in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20 and 40 and various censored schemes. The $AMLE_{II}$ is better than MLE and $AMLE_I$ in the sense of the MSE.

• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.425-445
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• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

• 한국음향학회지
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• 제20권8호
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• pp.58-66
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• 2001

본논문에서는 LFM (Linear Frequency Modulated) 신호를 사용하는 능동소나에서 적은 연산량으로 표적반사신호의 시간지연과 도플러를 추정하는 기법을 제안하였다. 제안한 기법에서는 일반적인 추정기법들이 가지는 연산량의 문제를 해결하기 위해 LFM 신호의 상호모호함수 (cross ambiguity function)에서 시간지연과 도플러의 관계를 나타내는 대수적인 관계식을 이용하였다. FML (Fast Maximum Likelihood) 기법을 기반으로 하여 시간지연과 도플러의 대수적 관계식을 유도하였으며, 이를 이용하여 일반적인 2차원 탐색 대신 2번의 1차원 탐색으로 시간지연과 도플러를 추정하였다. 다양한 신호대 잡음비 (SNR)에서 제안한 알고리즘의 추정오차를 분석하였으며, 제안한 알고리즘이 우수한 추정 성능을 보임을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권4호
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• pp.1063-1075
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• 2012

To locate an object accurately in the wireless sensor networks, the distance measure based on time-delay plays an important role. In this paper, we propose a maximum likelihood (ML) time-delay estimation algorithm in multi-path wireless propagation channel. We get the joint probability density function after sampling the frequency domain response of the multi-path channel, which could be obtained by the vector network analyzer. Based on the ML criterion, the time-delay values of different paths are estimated. Considering the ML function is non-linear with respect to the multi-path time-delays, we first obtain the coarse values of different paths using the subspace fitting algorithm, then take them as an initial point, and finally get the ML time-delay estimation values with the pattern searching optimization method. The simulation results show that although the ML estimation variance could not reach the Cramer-Rao lower bounds (CRLB), its performance is superior to that of subspace fitting algorithm, and could be seen as a fine algorithm.

• 한국통신학회논문지
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• 제27권6A호
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• pp.562-567
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• 2002

잡음 예측 최대 유사도(noise predictive maximum likelihood, NPML) 검출기는 잡음 예측/백색화 과정을 비터비 검출기의 가지 메트릭 계산 과정에 삽입하여 데이터 검출의 신뢰성을 높이게 된다. 따라서 기존의 PRML 검출기에 잡음 예측기를 포함시킴으로써 그것의 실제 성능이 향상되고 복잡도가 줄어드는 이점이 있다. 본 논문에서는 선형 채널과 비선형 채널 하에서 랜덤 시퀀스와 런-길이 제한 (1,7) 시퀀스를 적용하여, 고밀도 수직 자기 기록 (1.7$\leq$K$_{p}$$\leq$3.0)에서 잡음 예측 PR-등화 신호에 의한 NP(1221)ML 검출 시스템이 보다 높은 타수의 PR(12321)ML 시스템보다 복잡도가 낮으면서 우월한 성능을 나타냄을 모의 실험을 통해 분석, 검증하였다.

• Communications for Statistical Applications and Methods
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• 제23권2호
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.