• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1173-1181
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• 2008

In this paper, when the parameter of interest is the shape parameter in Pareto distribution, we develop likelihood based inference for this parameter. Specially, we develop signed log-likelihood ratio statistic and the modified signed log-likelihood ratio statistic for the shape parameter. It is well-known that as sample size grows, the modified signed log-likelihood ratio statistic converges to standard normal distribution faster than the signed log-likelihood ratio statistic. But the computation of the modified signed log-likelihood statistic is hard or even impossible when the sufficient statistics and the ancillary statistics are not clear. In this case, one can consider an approximation to the modified signed log-likelihood statistic. Specially, when the parameter of interest is informationally orthogonal to the nuisance parameters, we propose the approximate modified signed log-likelihood statistic. Through simulation, we investigate the performances of the proposed statistics with the signed log-likelihood statistic.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.613-618
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• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.633-645
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• 2018

Gaussian error distributions are a common choice in traditional regression models for the maximum likelihood (ML) method. However, this distributional assumption is often suspicious especially when the error distribution is skewed or has heavy tails. In both cases, the ML method under normality could break down or lose efficiency. In this paper, we consider the log-concave and Gaussian scale mixture distributions for error distributions. For the log-concave errors, we propose to use a smoothed maximum likelihood estimator for stable and faster computation. Based on this, we perform comparative simulation studies to see the performance of coefficient estimates under normal, Gaussian scale mixture, and log-concave errors. In addition, we also consider real data analysis using Stack loss plant data and Korean labor and income panel data.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1327-1334
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• 2008

Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2534-2542
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• 2011

In this paper, we show that performance can be improved by using multiple antennas in the conventional orthogonal code hopping multiplexing (OCHM) scheme, which was proposed for accommodating a larger number of users with low channel activities than the number of orthogonal codewords used in code division multiple access (CDMA)-based communication systems through downlink statistical multiplexing. First, we introduce two different types of OCHM systems together with orthogonal codeword allocation strategies, and then derive their mathematical expression for log-likelihood ratio (LLR) values according to the two different schemes. Next, when a turbo encoder based on the LLR computation is used, we evaluate performance on the frame error rate (FER) for the aformentioned OCHM system. For comparison, we also show performance for the existing symbol mapping method using multiple antennas, which was used in 3GPP standards. As a result, it is shown that our OCHM system with multiple antennas based on the proposed orthogonal codeword allocation strategy leads to performance gain over the conventional system---energy required to satisfy a target FER is significantly reduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7C
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• pp.616-626
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• 2007

In spatially multiplexed MIMO systems that enable high data rate transmission over wireless communication channels, the spatial demultiplexing at the receiver is a challenging task, and various demultiplexing methods have been developed recently by many researchers. Among the previous methods, maximum likelihood detection with QR decomposition and M-algorithm (QRM-MM)), and sphere decoding (SD) schemes have been reported to achieve a (near) maximum likelihood (ML) performance. In this paper, we propose a novel signal detection method that achieves a near ML performance in a computationally efficient manner. The proposed method is demonstrated via a set of computer simulations that the proposed method achieves a near ML performance while requiring a complexity that is comparable to that of the conventional MMSE-OSIC. We also show that the log likelihood ratio (LLR) values for all bits are obtained without additional calculation but as byproduct in the proposed detection method, while in the previous QRM-MLD, SD, additional computation is necessary after the hard decision for LLR calculation.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.495-504
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• 2015

The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe difficulty in computation because inference requires to invert a large covariance matrix in evaluating log-likelihood. In addressing this computational challenge, three strategies have been employed: likelihood approximation, lower dimensional space approximation, and Markov random field approximation. In this paper, we reviewed statistical approaches attacking the computational challenge. As an illustration, we also applied integrated nested Laplace approximation (INLA) technology, one of Markov approximation approach, to real data to provide an example of its use in practice dealing with large spatial data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.951-959
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• 2011

The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the efficient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1281-1284
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• 2009

무선통신에서는 다양한 서비스, 높은 신뢰도와 함께 빠른 전송속도를 요구한다. 이러한 요구를 만족시키기 위해서 LDPC 부호와 MIMO 기술이 활발히 연구 중에 있다. 본 논문에서는 LDPC와 결합된 STC 모델을 설명하고, 여러개의 송신안테나로부터 송신되어 결합된 신호를 분리하기 위해 사용되는 Log-Likelihood Computation을 기존의 방식과 제안하는 저 계산량 알고리즘을 통한 방식을 비교, 분석하여 기존의 방식과 근접한 BER 성능을 유지 하면서 계산량 감소를 확인한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2791-2797
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• 2010

In recent years, the goal of providing high speed wireless data services has generated a great amount of interest among the research community. Several researchers have shown that the capacity of the system, in the presence of flat Rayleigh fading, improves significantly with the use of combined MIMO and LDPC. To feed the soft values to LDPC decoder, the soft values must be calculated from multiple transmitter and receiver antennas in Rayleigh fading channel. It requires high computational complexity to get the soft symbols by increasing number of transmitter and receiver antennas. Therefore, this thesis proposed on effective algorithm for calculation of soft values from multiple antennas based on LLR. As result, This thesis shows that maximum 61% of computational complexity is reduced with a little loss of performance.