• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.65-76
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• 2008

Biplots are the multivariate analogue of scatter plots. They approximate the multivariate distribution of a sample in a few dimensions, typically two, and they superimpose on this display representations of the variables on which the samples are measured(Gower and Hand, 1996, Chapter 1). And the relationships between the observations and variables can be easily seen. Thus, biplots are useful for giving a graphical description of the data. However, this method does not give some concise interpretations between variables and observations when the number of observations are large. Therefore, in this study, we will suggest to interpret the biplot analysis by applying the K-means clustering analysis. It shows that the relationships between the clusters and variables can be easily interpreted. So, this method is more useful for giving a graphical description of the data than using raw data.

• 열린충남
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• s.47
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• pp.86-103
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• 2009

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.269-274
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• 2003

일반적으로 단순대응분석에서는 하나의 분할표 자료에 대한 행과 열의 대응관계만을 주로 다루어 왔으나 시점의 변화에 따른 행과 열 범주의 대응관계에 대한 변화의 추세를 나타내지는 못했다. 본 연구에서는 새로이 추가범주를 활용한 전이행렬자료의 동적 단순대응분석(dynamic simple correspondence analysis of transition matrix data: DSCA)을 제안하고자 한다. DSCA는 시점의 변화에 따른 행과 열 범주의 변화되는 대응관계뿐만 아니라 행 범주들의 시간적인 변화의 경향을 보여주는 장점을 갖고 있다. 또한 기준시점에서 다음 시점으로의 변화도 예측하여 보여줌으로써 향후 변화의 경향을 시각적으로 보여준다.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.559-566
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• 2011

Biplot is a useful graphical method to explore simultaneously rows and columns of two-way data matrix. In particular, canonical correlation biplot is a method for investigating two sets of variables and observations in canonical correlation analysis graphically. For more than three sets of variables, we can apply the generalized canonical correlation biplot in generalized canonical correlation analysis which is an expansion of the canonical correlation analysis. On the other hand, we consider the set of covariate variables which is affecting the linearly two sets of variables. In this case, if we apply the generalized canonical correlation biplot, we cannot clearly interpret the other two sets of variables due to the effect of the set of covariate variables. Therefor, in this paper, we will apply the partial canonical correlation analysis of Rao (1969) removing the linear effect of the set of covariate variables on two sets of variables. We will suggest the partial canonical correlation biplot for inpreting the partial canonical correlation analysis graphically.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.585-595
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• 2007

The squared Euclid distance of the values which is transformed by P-matrix of Ahn et al. (2003) is in proportion to the squared Euclid distance of cell's relative frequencies in two Contingency Tables. We propose the method of using the PP-values for the analysis of modern poems and questionnaire data.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.95-106
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• 2014

This paper analyses the economic effects of the water industry on the Korean economy by using Social Accounting Matrix (SAM). The SAM is constructed based on the Input-Output table, National account and Family income and expenditure survey for Korea in 2009. Through the SAM multiplier analysis, I estimate the effects of water investment. As the results, this study has found the followings. i) output multiplier effects of water sector are 5.300~7.741, ii) value added multiplier effects of water sector are 0.685~1.158, iii) income multiplier effects of water sector are 0.511~0.984, iv) redistributed income multiplier effects of water sector are -0.096~0.247. The results indicate that a significant influence on the industrial production and the household income in Korea.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.32-41
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• 2023

Recently, studies on reverberation suppression using Non-negative Matrix Factorization (NMF) have been actively conducted. The NMF method uses a cost function based on the Kullback-Leibler divergence for optimization. And some constraints are added such as temporal continuity, pulse length, and energy ratio between reverberation and target. The tendency of constraints are controlled by hyperparameters. Therefore, in order to effectively suppress reverberation, hyperparameters need to be optimized. However, related studies are insufficient so far. In this paper, the reverberation suppression performance according to the three hyperparameters of the NMF was analyzed by using sea experimental data. As a result of analysis, when the value of hyperparameters for time continuity and pulse length were high, the energy ratio between the reverberation and the target showed better performance at less than 0.4, but it was confirmed that there was variability depending on the ocean environment. It is expected that the analysis results in this paper will be utilized as a useful guideline for planning precise experiments for optimizing hyperparameters of NMF in the future.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1235-1243
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• 2010

This study proposes a trimmed LAD(least absolute deviation) estimators for multi-dimensional contingency tables and suggests an algorithm to estimate it. In addition, a method to determine the trimming quantity of the estimators is suggested. A Monte Carlo study shows that the propose method yields a better trimming rate and coverage rate than the previously suggest method based on the determinant of the covariance matrix.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.