• The Korean Journal of Applied Statistics
• /
• v.5 no.1
• /
• pp.103-111
• /
• 1992

The projection pursuit method has been gussested as a technique for the analysis of the multivariate data. This method seeks out interesting linear projections of the multivariate data onto a line of a plane to solve the curse or dimensionality. In this paper we developed the discriminant analysis by using the projection method and simulations were used for comparison between this and other existing discriminant analysis methods.

• Analytical Science and Technology
• /
• v.25 no.4
• /
• pp.214-222
• /
• 2012

ASTM (American Society for Testing and Materials) D6751-10 suggests analytical methods as well as specifications for biodiesel quality. However, it is expensive and time-consuming to follow the ASTM testing methods to analyze biodiesel and various impurities. This paper develops a quantitative analysis system for biodiesel and impurities based on Infrared spectroscopy and a multivariate statistical method, PLS (partial least squares). In addition, four different pre-processing techniques were compared for spectrum correction and noise reduction. Savitzky-Golay pre-processing showed the best performance.

• Proceedings of the Korean Statistical Society Conference
• /
• 2003.05a
• /
• pp.85-90
• /
• 2003

In this paper we develop a method for finding optimal ordering of K statistical models. This is based on a dependent paired comparison experimental arrangement whose results can naturally be represented by a completely oriented graph (also so called tournament graph). Introducing preference probabilities, strong transitivity conditions, and an optimal criterion to the graph, we show that a Hamiltonian path obtained from row sum ranking is the optimal ordering. Necessary theories involved in the method and computation are provided. As an application of the method, generalized variances of K multivariate normal populations are compared by a Bayesian approach.

• Communications for Statistical Applications and Methods
• /
• v.26 no.4
• /
• pp.371-383
• /
• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• The Korean Journal of Applied Statistics
• /
• v.35 no.3
• /
• pp.395-406
• /
• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.

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

• Proceedings of the Korean Statistical Society Conference
• /
• 2004.11a
• /
• pp.1-8
• /
• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Robin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method. A numerical example is also presented to illustrate the method.

• Korean Journal of Food Science and Technology
• /
• v.48 no.5
• /
• pp.418-423
• /
• 2016

This study was conducted to identify the geographical origin of soybeans in Cheonggukjang by analyzing its organic components and inorganic elements with Fourier transform near-infrared spectroscopy (FT-NIRS) and with energy dispersive X-ray fluorescence (ED-XRF) coupled with multivariate statistical analysis. For method development, 280 samples from various regions were collected and analyzed. The discriminant accuracy for the developed methods was 97.5% for FT-NIRS and 98.0% for ED-XRF with multivariate statistical analysis. A validation test confirmed the discriminant accuracy to be 96.3% for FT-NIRS and 95.0% for ED-XRF. Overall, the results showed that methods using FT-NIRS and ED-XRF could be used to identify the geographical origin of Cheonggukjang.

• The Korean Journal of Applied Statistics
• /
• v.35 no.6
• /
• pp.703-724
• /
• 2022

Although longitudinal studies mainly produce multivariate longitudinal data, most of existing statistical models analyze univariate longitudinal data and there is a limitation to explain complex correlations properly. Therefore, this paper describes various methods of modeling the covariance matrix to explain the complex correlations. Among them, modified Cholesky decomposition, modified Cholesky block decomposition, and hypersphere decomposition are reviewed. In this paper, we review these methods and analyze Korean children and youth panel (KCYP) data are analyzed using the Bayesian method. The KCYP data are multivariate longitudinal data that have response variables: School adaptation, academic achievement, and dependence on mobile phones. Assuming that the correlation structure and the innovation standard deviation structure are different, several models are compared. For the most suitable model, all explanatory variables are significant for school adaptation, and academic achievement and only household income appears as insignificant variables when cell phone dependence is a response variable.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 1998.11a
• /
• pp.316-321
• /
• 1998

In order to detect causal relationships between latent traits of sensual impressions for a color and physical characteristics constructing it, it is a common practice first to extract latent factors by a factor analysis method and secondly to clarify the causal relationships by a regression analysis method. This paper presents a multivariate statistical technique to detect the influence of the physical characteristics to the latent factors simultaneously which treats the physical characteristics as experimental factors in a $L_{27}$ factorial design and analysis the effects of the factors to the latent trait scores by an ANOVA.