• The Korean Journal of Applied Statistics
• /
• v.21 no.1
• /
• pp.19-33
• /
• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

• The Korean Journal of Applied Statistics
• /
• v.30 no.4
• /
• pp.567-578
• /
• 2017

Grouped multivariate data can be tested for differences between two or more groups using multivariate analysis of variance (MANOVA). However, this method cannot be used if several assumptions of MANOVA are violated. In this case, multidimensional scaling (MDS) and analysis of distance (AOD) can be applied to grouped dissimilarities based on the various distances. A permutation test is a non-parametric method that can also be used to test differences between groups. MDS is used to calculate the coordinates of observations from dissimilarities and AOD is useful for finding group structure using the coordinates. In particular, AOD is mathematically associated with MANOVA if using the Euclidean distance when computing dissimilarities. In this paper, we study the between and within group structure by applying MDS and AOD to the grouped dissimilarities. In addition, we propose a new test statistic using the group structure for the permutation test. Finally, we investigate the relationship between AOD and MANOVA from dissimilarities based on the Euclidean distance.

• The Korean Journal of Applied Statistics
• /
• v.35 no.5
• /
• pp.579-591
• /
• 2022

Functional data is collected in various fields. It is often necessary to test whether there are differences among groups of functional data. In this case, it is not appropriate to explain using the point-wise ANOVA method, and we should present not the point-wise result but the integrated result. Various studies on functional data analysis of variance have been proposed, and recently implemented those methods in the package fdANOVA of R. In this paper, I first explain ANOVA and multivariate ANOVA, then I will introduce various methods of analysis of variance for univariate and multivariate functional data recently proposed. I also describe how to use the R package fdANOVA. This package is used to test equality of weekly temperatures in Seoul and Busan through univariate functional data ANOVA, and to test equality of multivariate functional data corresponding to handwritten images using multivariate function data ANOVA.

• Proceedings of the KSEEG Conference
• /
• 2005.04a
• /
• pp.235-237
• /
• 2005

• The Korean Journal of Applied Statistics
• /
• v.27 no.5
• /
• pp.809-818
• /
• 2014

Multivariate skew-normal distribution(distribution that includes multivariate normal distribution) has been recently applied to many application areas. We consider saddlepoint approximation for a statistic of linear combination based on a multivariate skew-normal distribution. This approach can be regarded as an extension of Na and Yu (2013) that dealt saddlepoint approximation for the distribution of a skew-normal sample mean for a linear statistic and multivariate version. Simulations results and examples with real data verify the accuracy and applicability of suggested approximations.

• Communications for Statistical Applications and Methods
• /
• v.3 no.1
• /
• pp.131-143
• /
• 1996

다변량 층화임의추출에서 한 변수의 Neyman 최적배분은 다른 변수에 대한 층화분산을 최소화시키지 못하는 결과를 초래할 수도 있다. 따라서 다변량 자료의 경우 '최적'배분 대신에 '절충'배분이 도입되어 왔다. 이 연구에서는 각 변수별 Neyman 최적배분에 근거해서 얻은 층화표본평균벡터의 공분산 행렬에 가장 잘 적합되는 층별로 동일한 크기의 절충배분을 찾고자 한다. 이에 적절한 기준 다섯가지를 제시하고 예를 통해 비교, 분석하였다.

• The Korean Journal of Applied Statistics
• /
• v.13 no.2
• /
• pp.383-392
• /
• 2000

Since late 1970, methods of influence or sensitivity analysis for detecting influential observations have been studied not only in regression and related methods but also in various multivariate methods. If results of multivariate analyses sometimes depend heavily on a small number of observations, we should be very careful to draw a conclusion. Similar phenomena may also occur in the case of incomplete data. In this research we try to study such influential observations in multivariate statistical analysis of incomplete data. Case of principal component analysis is studied with a numerical example.

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.821-831
• /
• 2011

It has been relatively incomplete in the field of financial time series to adapt asymmetric features to multivar ate GARCH processes (McAleer et al., 2009). Retaining constant conditional correlation(CCC) structure, this article pursues to introduce asymmetric GARCH modelling in analysing multivariate volatilities in time series in a practical point of view. Multivariate Korean financial time series are analyzed in detail to compar our theory with conventional methodologies including GARCH and EGARCH.

• Journal for History of Mathematics
• /
• v.10 no.2
• /
• pp.48-52
• /
• 1997

통계학이란 미래에 대한 예측을 하고 이에 대비하여 합리적인 의사결정을 내리는데 도움을 받을 수 있는 학문이다. 최근 다변량 통계분석은 관찰이나 실험의 대상이 되는 하나 이상의 변수들을 동시에 분석할 수 있는 매우 실제성이 높은 분석방법으로 통계학, 경영학, 사회학, 심리학, 생물학 등 여러 전공 분야에서 복잡하고 다양한 자료 분석에 폭넓게 활용되고 있다. 이 논문에서는 다변량 분석 방법 중 컴퓨터와 통계 분석 소프트웨어의 발전으로 인하여 최근에 활발히 연구되고 있는 군집방법의 역사와 여러 연구분야의 실제자료분석에 응용할 수 있도록 군집분석을 6가지로 나누어서 분류하였고 그 방법론을 제시하였다.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1421-1428
• /
• 2016

This paper concerns the freshness of rice depending on packing materials using MANOVA. Freshness of rice is measured in terms of moisture content and rice flavor. Ordinary paper and charcoal-coated paper are compared as packing materials. Storing places are considered as a block. The bivariate observations of moisture content and the rice flavor are compared using MANOVA for a completely randomized block design. It is observed that there is a significant difference between ordinary paper and charcoal-coated paper. Therefore we apply ANOVA for moisture content and rice flavor, respectively. Significant differences are observed for the moisture content but not for the rice flavor.