• Communications for Statistical Applications and Methods
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• 제18권1호
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• pp.13-21
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• 2011

본 논문에서는 계절성을 가지는 다변량 비정상 시계열자료의 분석 방법을 연구한다. 이를 위하여, 3가지의 다변량 시계열분석 모형(계절형 공적분 모형, 계절형 가변수를 가지는 비계절형 공적분 모형, 차분을 이용한 벡터자기회귀모형)을 고려하고, 한국의 실제 거시경제 자료를 이용하여 3가지 모형의 예측력을 비교한다. 공적분 모형은 단기적 예측에서 우수하였고, 장기적 예측에서는 차분을 이용한 벡터자기회귀모형이 우수하였다.

• 응용통계연구
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• 제25권4호
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• pp.589-603
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• 2012

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

• Journal of the Korean Statistical Society
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• 제31권4호
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• pp.519-532
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• 2002

K-means clustering is a well-known partitioning method of multivariate observations. Recently, the method is implemented broadly in data mining softwares due to its computational efficiency in handling large data sets. However, it does not yield a suitable visual display of multivariate observations that is important especially in exploratory stage of data analysis. The aim of this study is to develop a K-means clustering method that enables visual display of multivariate observations in a low-dimensional space, for which the projection pursuit method is adopted. We propose a computationally inexpensive and reliable algorithm and provide two numerical examples.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.44-50
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• 2024

This paper offers an overview of matrix formation and calculation techniques within the framework of General Linear Models (GLMs). It takes a sequential approach, beginning with a detailed exploration of matrix formation and calculation methods in regression analysis and univariate analysis of variance (ANOVA). Subsequently, it extends the discussion to cover multivariate analysis of variance (MANOVA). The primary objective of this study was to provide a clear and accessible explanation of the underlying matrices that play a crucial role in GLMs. Through linking, essentially different statistical methods, by fundamental principles and algebraic foundations that underpin the GLM estimation. Insights presented here aim to assist researchers, statisticians, and data analysts in enhancing their understanding of GLMs and their practical implementation in diverse research domains. This paper contributes to a better comprehension of the matrix-based techniques that can be extended to GLMs.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.15-35
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• 2020

In research on behavioral studies, significant attention has been paid to the stage-sequential process for multiple latent class variables. We now explore the stage-sequential process of multiple latent class variables using the multivariate latent class profile analysis (MLCPA). A latent profile variable, representing the stage-sequential process in MLCPA, is formed by a set of repeatedly measured categorical response variables. This paper proposes the extended MLCPA in order to explain an association between the latent profile variable and the latent group variable as a form of a two-dimensional contingency table. We applied the extended MLCPA to the National Longitudinal Survey on Youth 1997 (NLSY97) data to investigate the association between of developmental progression of depression and substance use behaviors among adolescents who experienced Authoritarian parental styles in their youth.

• Computers and Concrete
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• 제13권2호
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• pp.173-185
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• 2014

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

• Journal of Korean Neurosurgical Society
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• 제40권4호
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• pp.304-308
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• 2006

Statistical analyses used in clinical articles published on the Journal of Korean Neurosurgical Society were identified and appropriateness of statistical aspects in reporting results was assessed. Forty seven clinical articles were selected in this study, which were published from February, 2005 to February, 2006 on the journal. The frequency of statistical analysis was as follows : descriptive statistics only 24 [51.1%]. one type of statistical method 10 [21.3%], two or more methods 13 [27.6%]. An assessment of statistical aspects was performed in 24 clinical articles reporting inferential statistics. Ten articles [41.7%] did not adequately describe or reference all statistical methods used. There were six articles [25.0%] not reporting the confidence level used as the critical criteria of the statistical significance. In thirteen articles [54.2%] it seems more appropriate to implement multivariate analyses in addition to univariate analyses. We recommend that the journal readers should concentrate on improving their knowledge of basic statistics and statistical review for manuscripts submitted should be sought from professionals in the fields of biostatistics and epidemiology.

• Communications for Statistical Applications and Methods
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• 제18권1호
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• pp.57-69
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• 2011

질병에 대한 새로운 진단검사 방법이 의학 연구자들에 의해 끊임없이 개발되고 있으며, 기존 진단검사 방법과 새로운 진단검사 방법을 비교하는 연구논문이 계속 출간되어 누적되고 있다. 메타분석법으로 다수 연구논문의 결과를 종합하여 정확성이 높은 진단검사에 대해 객관적인 결론을 내리게 된다. 이와같이 출간된 두 진단검사를 비교하는 각 연구논문은 각각 질병을 가진 개체와 질병을 가지지 않은 개체에 두 검사를 모두 실시하여 한 쌍의 민감도와 특이도를 구하여 비교한다. 이러한 연구논문의 결과를 종합하는 메타분석은 동일 개체에 실시한 두 검사로 인해 한 쌍의 민감도간의 연관성과 한 쌍의 특이도 간의 연관성을 고려한 메타분석법을 본 논문에서 제시한다. 논문예제 자료와 모의시험으로 메타분석 검정통계량의 효율성을 평가한다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.91-95
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• 2004

Moments of skew t random vectors and their quadratic forms are derived. It is shown that the moments of the sample autocovariance function and of the sample variogram estimator do not depend on a skewness parameter.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.49-63
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• 1997

We consider the problem of classifying a p x 1 observation into one of two multivariate normal populations when the training smaples contain a block of missing observation. A new classification procedure is proposed which is a linear combination of two discriminant functions, one based on the complete samples and the other on the incomplete samples. The new discriminant function is easy to use.