• Journal of the Korean Magnetic Resonance Society
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• v.15 no.2
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• pp.157-174
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• 2011

Although the use of apodization functions in connection with postprocessing of a 2D NMR spectrum proves improved spectral quality, there is usually a trade-off between resolution enhancement and noise suppression due to a classical "uncertainty principle." In this study, therefore, a mathematical deconvolution technique called "Maximum Likelihood Deconvolution (MLD)" was adopted to achieve the spectral resolution and sensitivity enhancement simultaneously. The MLD technique greatly facilitates visualization and restoration of the genuine spectral information from complex 2D NMR spectra that would be problematic with the conventional apodization/FT processing. In particular, application of the MLD to the 2D-NOE spectrum would be very useful to derive the important proton connectivities, which are essential to achieve elucidating the 3D molecular structure.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.137-143
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• 2011

The classification of subjects with unknown distribution in a small sample size often involves order-restricted constraints in multivariate parameter setups. Those problems make the optimality of a conventional likelihood ratio based statistical inferences not feasible. Fortunately, Roy (1953) introduced union-intersection principle(UIP) which provides an alternative avenue. Multivariate linear rank statistics along with that principle, yield a considerably appropriate robust testing procedure. Furthermore, conditionally distribution-free test based upon exact permutation theory is used to generate p-values, even in a small sample. Applications of this method are illustrated in a real microarray data example (Lobenhofer et al., 2002).

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• The Korean Journal of Ecology
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• v.26 no.2
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• pp.75-81
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• 2003

This study was performed to prove if the high resolution satellite imagery of IKONOS is suitable for preparing digital vegetation map which is becoming increasingly important in ecological science. Seven classes for forest area and five classes for non-forest area were taken for classification. Three methods, such as the pixel based classification, the segment based classification with majority principle, and the segment based classification with maximum likelihood, were applied to classify IKONOS imagery taken in April 2000. As a whole, the segment based classification shows better performance in classifying the high resolution satellite imagery of IKONOS. Through the comparison of accuracies and kappa values of the above 3 classification methods, the segment based classification with maximum likelihood was proved to be the best suitable for preparing the vegetation map with the help of IKONOS imagery. This is true not only from the viewpoint of accuracy, but also for the purpose of preparing a polygon based vegetation map. On the basis of the segment based classification with the maximum likelihood, a digital vegetation map in which each vegetation class is delimitated in the form of a polygon could be prepared.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1522-1532
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• 1996

In this paper, we compare the performance of channel estimators with the L$_{1}$-norm and L$_{2}$-norm criteria in impulaive noise environment, and show than the L$_{1}$-norm criterion is appropriate for that situation. Also, it is shown that the performance of the conventional maximum likelihood sequence detector(MLSD) can be improved by applying the same principle to mobile channels. That is, the performance of the conventional MLSD, which is known to be optimal under the Gaussian noise assumption, degrades in the impulsive noise of radio mobile communication channels. So, we proposed the MLSD which can reduce the effect of impulsive noise effectively by applying the results of channel estimators. Finally, it is confirmed by computer simulation that the performance of MLSD is significantly affected depending on the types of branch metrics, and that, in the impulsive noise environments, the proposed one with new branch metrics performs better thatn the conventional branch metric, l y(k)-s(k) l$^{[-992]}$ .

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1780-1783
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• 2002

A problem of signal transmitted and received in OFDM systems is considered. In particular, an efficient solution to the problem of blind channel estimation based on Maximum Likelihood (ML) principle has been investigated. The paper proposes a new upper-bound cost, used in conjunction with a standard branch and bound integer programming technique for solving the ML problem. The tighter upper-bound cost exploits a finite-alphabet property of the transmitted signal. The proposed upper-bound cost was found to greatly speed up the ML algorithm, thus reducing computational complexity. Experimental results and discussion are included.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.198-200
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• 2003

This study proved if the high resolution satellite imagery of IKONOS is suitable for preparing digital forest cover map. Three methods, the pixel based classification with maximum likelihood (PML), the segment based classification with majority principle(SMP), and the segment based classification with maximum likelihood(SML), were applied to classify and delimitate forest cover of IKONOS imagery taken in May 2000 in a forested area in the central Korea. The segment-based classification was more suitable for classifying and deliminating forest cover in Korea using IKONOS imagery. The digital forest cover map in which each class is delimitated in the form of a polygon can be prepared on the basis of the segment-based classification.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.57-59
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• 1999

본 논문에서는 최대 엔트로피 원리에 기반한 문서 분류기의 학습을 제안한다. 최대 엔트로피 기법은 자연언어 처리에서 언어 모델링(Language Modeling), 품사 태깅 (Part-of-Speech Tagging) 등에 널리 사용되는 방법중의 하나이다. 최대 엔트로피 모델의 효율성을 위해서는 자질 선정이 중요한데, 본 논문에서는 자질 집합의 선택을 위한 기준으로 chi-square test, log-likelihood ratio, information gain, mutual information 등의 방법을 이용하여 실험하고, 전체 후보 자질에 대한 실험 결과와 비교해 보았다. 데이터 집합으로는 Reuters-21578을 사용하였으며, 각 클래스에 대한 이진 분류 실험을 수행하였다.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.325-334
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• 2009

In this research, we propose a nonparametric test procedure for the right censored and grouped data under the additive hazards model. For deriving the test statistics, we use the likelihood principle. Then we illustrate proposed test with an example and compare the performance with other procedure by obtaining empirical powers. Finally we discuss some interesting features concerning the proposed test.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.117-127
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• 1991

Coefficient of determination $R^2$ is most frequently used descriptive measure in practical use of linear regression analysis. But there have been controversies on defining this measure in the cases of linear regression without the intercept, weighted linear regression and robust linear regression. Several authors such as Kvalseth(1985) and Willet and Singer(1988) proposed many variations of $R^2$ to meet the situations. However, theire measures are not satisfactory due to the lack of a universal principle. In this study, we propose a unfied approach to defining the coefficient of determination $R^2$ using the concept of likelihood distance. This new measure is in good accordance with typical $R^2$ in linear regression and, moreover, can be applied to nonlinear regression models and generalized linear models such as logit and log-linear models.