• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.427-439
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• 2017

Quade (1967) proposed RANK ANCOVA, which is a nonparametric method to test differences between treatments when there are covariates. Hwang and Kim (2012) also proposed a joint placement test on covariate-adjusted residuals. In this paper, we proposed a new nonparametric method to control the effect of covariate on a response variable that uses linear statistics on covariate adjusted-residuals. The score function used in the linear statistics was proposed by Jeon and Kim (2016). Monte Carlo simulation is also conducted to compare the empirical powers of the proposed method with previous methods.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.121-133
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• 2015

This tutorial presents an approach to perform the covariance based structural equation modeling using the R. For this purpose, the tutorial defines the criteria for the covariance based structural equation modeling by reviewing previous studies, and shows how to analyze the research model with an example using the "lavaan" which is the R package supporting the covariance based structural equation modeling. In this tutorial, a covariance-based structural equation modeling technique using the R and the R scripts targeting the example model were proposed as the results. This tutorial will be useful to start the study of the covariance based structural equation modeling for the researchers who first encounter the covariance based structural equation modeling and will provide the knowledge base for in-depth analysis through the covariance based structural equation modeling technique using R which is the integrated statistical software operating environment for the researchers familiar with the covariance based structural equation modeling.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.333-342
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• 2008

Variable selection theorem in the linear regression model is extended to the analysis of covariance model. When some of regression variables are omitted from the model, it reduces the variance of the estimators but introduces bias. Thus an appropriate balance between a biased model and one with large variances is recommended.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1076-1082
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• 2013

The frequency bands were discovered which maximize the slopes of autocovariances of speech signals in frequency domain to increase the possibility of segregation between speech signals and background noise signal. A speech signal is divided into blocks which include multiples of sampled data, then those blocks are transformed to frequency domain using Fast Fourier Transform(FFT). To find linear equation by Linear Regression, the coefficients of autocovariance within blocks of some frequency band are used. The slope of the linear equation which is called the slope of autocovariance is varied from band to band according to the characteristics of the speech signal. Using speech signals of a man which consist of 200 files, the coefficients of the slopes of autocovariances are analyzed and compared from band to band.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.36-43
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• 2009

In this study, the impact of the covariance between the baselines on the network-based GPS positioning is analyzed. For the analysis, the multi-baseline solutions with properly modeled covariance between the baselines and the combined solutions from the single-baseline solutions are obtained, respectively. Then, the accuracies of both solutions are evaluated in terms of coordinate residuals, i.e., the differences between the positioning solutions and the published stations' coordinates. The results indicate that the positioning accuracy in static mode depends much on the geometry of GPS satellites rather than the proper modeling of covariance between the baselines. Also, slight but negligible improvement in positioning accuracy is observed in static solutions. Therefore, one may use combined solutions as an alternative to multi-baseline solutions for the network-based GPS positioning. However, multi-baseline solution with properly modeled covariance between the baselines is recommended to use especially for the applications to detect very small displacement, i.e., deformation of the building or bridge.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.747-758
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• 2013

The covariance matrix is important in multivariate statistical analysis and a sample covariance matrix is used as an estimator of the covariance matrix. High dimensional data has a larger dimension than the sample size; therefore, the sample covariance matrix may not be suitable since it is known to perform poorly and event not invertible. A number of covariance matrix estimators have been recently proposed with three different approaches of shrinkage, thresholding, and modified Cholesky decomposition. We compare the performance of these newly proposed estimators in various situations.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.103-117
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• 2017

In longitudinal data analysis, the serial correlation of repeated outcomes must be taken into account using covariance matrix. Modeling of the covariance matrix is important to estimate the effect of covariates properly. However, It is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome the restrictions, several Cholesky decomposition approaches for the covariance matrix were proposed: modified autoregressive (AR), moving average (MA), ARMA Cholesky decompositions. In this paper we review them and compare the performance of the approaches using simulation studies.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.127-130
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• 2004

기존의 전대역(Full-Band)에서 특징 파라미터를 추출하는 화자 확인(Speaker Verification) 시스템은 저대역이나 고대역에서 화자 정보의 특징이 제거되기 쉽다. 또한, 주파수 스펙트럼에 부분적으로 오염이 되는 경우, 특징 파라미터를 왜곡시켜 화자 확인 시스템의 성능을 저하시킨다. 본 논문에서는 이러한 문제점을 해결하기 위해 다중대역 공분산 모델(Covariance Model)을 제안한다. 제안한 방법은 주파수 영역에서 전대역을 여러 개의 부대역(Sub-Band)으로 분할하고, 부대역별로 독립적으로 특징 파라미터를 추출하여 공분산 모델을 구한다. 제안된 방법의 성능 확인을 위하여 공분산 모델 간의 거리를 측정하는 화자 확인 실험을 하였다. 잡음 환경에서 기존의 방법인 전대역에 기반한 공분산 모델과 제안한 방법을 비교 분석한 결과, 제안한 방법이 기존 방법보다 $2\%$정도 성능이 향상되었다. 또한, 제안된 방법은 전대역에 기반한 파라미터 차원 수를 다중대역의 개수로 분할하여 사용하므로 계산량의 감소와 저장 공간면에서 효율적이다.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.60-65
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• 2016

This paper introduces the non-local means (NLM) algorithm for image denoising, and also introduces an improved algorithm which is based on the principal component analysis (PCA). To do the PCA, a covariance matrix of a given image should be evaluated first. If we let the size of neighborhood patches of the NLM S × S², and let the number of pixels Q, a matrix multiplication of the size S² × Q is required to compute a covariance matrix. According to the characteristic of images, such computation is inefficient. Therefore, this paper proposes an efficient method to compute the covariance matrix by sampling the pixels. After sampling, the covariance matrix can be computed with matrices of the size S² × floor (Width/l) × (Height/l).