• Journal of the Korean Magnetic Resonance Society
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• v.17 no.1
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• pp.1-10
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• 2013

Metabolomics is the study which detects the changes of metabolites level. Metabolomics is a terminal view of the biological system. The end products of the metabolism, metabolites, reflect the responses to external environment. Therefore metabolomics gives the additional information about understanding the metabolic pathways. These metabolites can be used as biomarkers that indicate the disease or external stresses such as exposure to toxicant. Many kinds of biological samples are used in metabolomics, for example, cell, tissue, and bio fluids. NMR spectroscopy is one of the tools of metabolomics. NMR data are analyzed by multivariate statistical analysis and target profiling technique. Recently, NMR-based metabolomics is a growing field in various studies such as disease diagnosis, forensic science, and toxicity assessment.

• Journal of the Korean Chemical Society
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• v.63 no.2
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• pp.78-84
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• 2019

High-resolution $^1H$ NMR spectroscopic technique has been widely used as one of the most powerful analytical tools in food chemistry as well as to define molecular structure. The $^1H$ NMR spectra-based metabolomics has focused on classification and chemometric analysis of complex mixtures. The principal component analysis (PCA), an unsupervised clustering method and used to reduce the dimensionality of multivariate data, facilitates direct peak quantitation and pattern recognition. Using a combination of these techniques, the various green teas and black teas brewed were investigated via metabolite profiling. These teas were characterized based on the leaf size and country of cultivation, respectively.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.267-279
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• 2021

A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Sufficient dimension reduction provides effective tools for the reduction, but there are few sufficient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.7-13
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• 2023

In this paper, Depression is a mental disorder characterized by a lack of enthusiasm and feelings of sadness, which significantly impairs daily functioning. In 2018, there was an increase in book sales in the essay genre, particularly the popularity of "healing essays." This trend is seen as challenging the negative image and prejudices associated with depression. In 2021, a significant rise in the proportion of 20-year-old patients with depression is attributed to factors like job-related stress, interpersonal issues, and financial burdens. Additionally, there is a strong correlation between depression and suicidal thoughts, particularly among individuals who have experienced feelings of depression. Despite the increasing prevalence of depression among young adults, research in this area is lacking. To address this gap, statistical tools such as logistic regression and chi-squared tests are employed. The analysis reveals various independent variables associated with feelings of depression, shedding light on the relationships between these factors.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.465-474
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• 2004

Data visualization is the automation process and the discovery process to data sets in an effort to discover underlying information from the data. It provides rich visual depictions of the data. It has distinct advantages over traditional data analysis techniques such as exploring the structure of large scale data set both in the sense of number of observations and the number of variables by allowing great interaction with the data and end-user. We discuss the principles of data visualization and evaluate the characteristics of various tools of visualization according to these principles.

• Journal of the Korean earth science society
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• v.35 no.4
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• pp.225-236
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• 2014

Multivariate statistical analyses have been extensively applied to hydrochemical measurements to analyze and interpret the data. This study examines anthropogenic factors obtained from applications of correspondence analysis (CA) and principal component analysis (PCA) to a hydrogeochemical data set. The goal was to synthesize the hydrogeochemical information using these multivariate statistical techniques by incorporating hydrogeochemical speciation results calculated by the program, commonly used, WATEQ4F included in the NETPATH. The selected case study was LPG underground storage caverns, which is located in the southeastern Korea. The highly alkaline groundwaters at this study area are an analogue for the repository system. High pH, speciation of Al and possible precipitation of calcite characterize these groundwaters. Available groundwater quality monitoring data were used to confirm these statistical models. The present study focused on understanding the hydrogeochemical attributes and establishing the changes of phase when two anthropogenic effects (i.e., disinfection activity and cement pore water) in the study area have been introduced. Comparisons made between two statistical results presented and the findings of previous investigations highlight the descriptive capabilities of PCA using calculated saturation index and CA as exploratory tools in hydrogeochemical research.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.59-79
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• 2021

Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

• Journal of Biosystems Engineering
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• v.41 no.1
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• pp.51-59
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• 2016

Purpose: This study examined the performance of two spectroscopy methods and multivariate classification methods to discriminate viable pepper seeds from their non-viable counterparts. Methods: A classification model for viable seeds was developed using partial least square discrimination analysis (PLS-DA) with Fourier transform near-infrared (FT-NIR) and Raman spectroscopic data in the range of $9080-4150cm^{-1}$ (1400-2400 nm) and $1800-970cm^{-1}$, respectively. The datasets were divided into 70% to calibration and 30% to validation. To reduce noise from the spectra and compare the classification results, preprocessing methods, such as mean, maximum, and range normalization, multivariate scattering correction, standard normal variate, and $1^{st}$ and $2^{nd}$ derivatives with the Savitzky-Golay algorithm were used. Results: The classification accuracies for calibration using FT-NIR and Raman spectroscopy were both 99% with first derivative, whereas the validation accuracies were 90.5% with both multivariate scattering correction and standard normal variate, and 96.4% with the raw data (non-preprocessed data). Conclusions: These results indicate that FT-NIR and Raman spectroscopy are valuable tools for a feasible classification and evaluation of viable pepper seeds by providing useful information based on PLS-DA and the threshold value.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.