• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.516-522
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• 2017

In this paper, we propose a maximum likelihood signal detection scheme for a generalized spatial modulation system that activates only a subset of transmit antennas among multiple antennas and transmits information through the indexes of active antennas as well as through the transmit symbols. The proposed maximum likelihood receiver extracts a set of candidate solutions based on their a posteriori probabilities to lower the computational load of the robust receiver under channel information errors. Then, the chosen candidate solutions are exploited to estimate the covariance matrix of effective noise. Simulation results show that the proposed maximum likelihood detection scheme achieves better error performance than a receiver that does not take into account the channel information errors. It is also seen that it reduces the computational complexity with the same bit error rate performance as the conventional robust maximum likelihood receiver.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.85-90
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• 2003

이 논문에서는 Samuel, Pignatiello와 Calvin(1998)이 제안한 ${\overline{X}}$ 관리도에서 이상원인 발생시점에 대한 최대우도추정량에 기초하여 변량표본크기(VSS) ${\overline{X}}$ 관리도를 수행하는 경우에 사용할 수 있는 최대우도추정량을 제안한다. 또한 제안된 최대우도추정량을 이용하여 이상원인 발생 시점에 대한 신뢰구간을 설정하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.165-170
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• 2013

In spatial, desired target direction of arrival estimation is to find a incidental signal direction on receive antennas. In this paper, we were an estimation a desired target direction of arrival using maximum likelihood method. Direction of arrival estimation method estimated a desired target calculating the maximum likelihood sensitivity using singular value decomposition above threshold signals among receive signals in maximum likelihood method. Through simulation, we were analysis a performance to compare existing method and proposal method. In direction of arrival estimation, proposed method is effectivity to decrease processing time because it is not doing an eigen decomposition in direction of arrival estimation, and desired target correctly estimated. We showed that proposal method improve more target estimation than general method.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.67-77
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• 2010

In multinomial scheme with step-wise sampling, maximum likelihood estimates of multinomial probabilities are improved when some frequencies are merged. In this study, for cell probabilities in a I by J independent contingency tables, exact MLE and step-wise estimation methods are applied and the results are compared using MSE and Bias.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.7-12
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• 2010

In this paper, we analyze and simulate the performance of a cooperative communication system adopting a maximum likelihood (ML) equalizer. In wireless communication systems, cooperative communication schemes employing several relays can be applied for extending the communication coverage. It is assumed that both relays and user terminals can move. Therefore, coverages of two or more relays can overlap each other. If wanted and interfering signals are transmitted through the same channel and there are one terminal in the overlapped region, its performance is degraded due to interference. Hence, we use a ML equalizer for rejecting the effect of interfering signal and enhancing the communication system performance. The cooperative system performance is evaluated in terms of bit error probability. From the simulation results, it is demonstrated that the ML receiver shows good interference cancellation performance although its complexity is high.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.391-398
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• 2023

In this paper, a penalized maximum likelihood estimation (PMLE) method that applies a penalty to increase the accuracy of a basis-screening-based Kriging model (BSKM) is introduced. The maximum order and set of basis functions used in the BSKM are determined according to their importance. In this regard, the cross-validation error (CVE) for the basis functions is employed as an indicator of importance. When constructing the Kriging model (KM), the maximum order of basis functions is determined, the importance of each basis function is evaluated according to the corresponding maximum order, and finally the optimal set of basis functions is determined. This optimal set is created by adding basis functions one by one in order of importance until the CVE of the KM is minimized. In this process, the KM must be generated repeatedly. Simultaneously, hyper-parameters representing correlations between datasets must be calculated through the maximum likelihood evaluation method. Given that the optimal set of basis functions depends on such hyper-parameters, it has a significant impact on the accuracy of the KM. The PMLE method is applied to accurately calculate hyper-parameters. It was confirmed that the accuracy of a BSKM can be improved by applying it to Branin-Hoo problem.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.273-282
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• 2009

Missing data is one of the common problems in building analysis or prediction models using software project data. Missing imputation methods are known to be more effective missing data handling method than deleting methods in small software project data. While K nearest neighbor imputation is a proper missing imputation method in the software project data, it cannot use non-missing information of incomplete project instances. In this paper, we propose an approach to missing data imputation for numerical software project data by combining K nearest neighbor and maximum likelihood estimation; we also extend the average absolute error measure by normalization for accurate evaluation. Our approach overcomes the limitation of K nearest neighbor imputation and outperforms on our real data sets.

• Journal of Korean Society of Rural Planning
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• v.12 no.1 s.30
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• pp.75-83
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• 2006

본 연구에서는 최대우도법과 인공신경망 모형에 의해 카테고리 분류를 수행하고 각각의 분류 성능을 비교 평가하였다. 인공신경망 모형은 오류역전파 알고리즘을 이용한 것으로서 학습을 통한 은닉층의 최적노드수를 결정하여 카테고리 분류를 수행하도록 하였다. 인공신경망 최적 모형은 입력층의 노드수가 7개, 은닉층의 최적노드수가 18개, 그리고 출력층의 노드수가 5개인 것으로 구성하였다. 위성영상은 1996년에 촬영된 Landsat TM-5 영상을 사용하였고, 최대우도법과 인공신경망 모형에 의한 카테고리 분류를 위하여 각각의 카테고리에 대한 분광특성을 대표하는 지역을 절취하였다. 분류 정확도는 인공신경망 모형에 의한 방법이 90%, 최대우도법이 83%로서, 인공신경망 모형의 분류 성능이 뛰어난 것으로 나타났다. 카테고리 분류 항목인 토지 피복 상태에 따른 분류는 두 가지 방법에서 밭과 주거지의 분류오차가 큰 것으로 나타났다. 특히, 최대우도법에 의한 밭에서의 태만오차는 62.6%로서 매우 큰 값을 보였다. 이는 밭이나 주거지의 특성이 위성영상 촬영시기에 따라 나지의 형태로 분류되거나 산림, 또는 논으로도 분류되는 경향이 있기 때문인 것으로 보인다. 차후에 카테고리 분류를 위한 각각의 클래스의 보조적인 정보를 추가한다면, 카테고리 분류 향상이 이루어질 것으로 기대된다.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.229-238
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• 2009

We propose a simple algorithm for obtaining MTL(maximum trimmed likelihood) estimates. The algorithm finds the subset to use to obtain the global maximum in the series of eliminating process which depends on the likelihood of cells in a contingency table. To evaluate the performance of the algorithm for MTL estimators, we conducted simulation studies. The results showed that the algorithm is very competitive in terms of computational burdens required to get the same or the similar results in comparison with the complete enumeration.