• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.103-125
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• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.561-575
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• 2023

Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Magnetoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static/temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network differences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.

• Journal of Korean Academy of Nursing
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• v.41 no.3
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• pp.354-363
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• 2011

Purpose: The purpose of this cross-sectional study was to examine the relationship between characteristics of severe ALS patient-caregiver couples and health related quality of life (HRQoL) in family caregivers. Methods: The participants in this study were 89 pairs of ALS patients using ventilators and a family caregiver. The characteristics of the ALS patients and caregivers, Korean-Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised, Zarit Burden Interview and SF-36 were measured in this study. The data were collected from August 2008 to April 2009. Descriptive statistics, Pearson correlation coefficients, and canonical correlation were used for data analysis. Results: The physical component summary and mental component summary of the HRQoL score for family caregivers were $147.49{\pm}31.63$ and $129.09{\pm}35.83$, respectively. HRQoL for caregivers was related to characteristics of the ALS patient-caregiver couples, such as patient's gender, caregiver's age, gender, marital status, daily time spent in caregiving and burden with one significant canonical variable. The significant variate showed that the lower the age, the time spent in caregiving and the burden of caregivers, the higher the HRQoL of caregivers. Conclusion: The support systems for caregivers considering caregiver characteristics such as demographics and burden should be implemented to improve the HRQoL of caregivers.

• Journal of Korean Academy of Nursing
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• v.27 no.2
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• pp.253-263
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• 1997

This second study on the effectiveness of nursing organization was designed to test the relationships between effectiveness of nursing organizations and structural variables that had been significant variables in the first study, the group design variable and the personal characteristic variables that had not been analyszed in the first study based on personal resource productivity model. The data were collected through self-reported questionnaires completed by 605 nurses working in hospitals in seoul and 782 patients being hospitalized in 5 tertiary hospitals in Seoul. The results showed that according to the canonical correlation analysis, the managing job design, nursing delivery system. nurse's age, career. and formalization were revealed as predicting variables of a nurses' job satisfaction and patients satisfaction among the five hospitals. Hospitals in which the team nursing method was used showed a higher score in nurses' job satisfaction and patient satisfaction than in hospitals which used the functional nursing model.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.247-262
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• 2024

In this paper, we explore nonlinear sufficient dimension reduction (SDR) methods, with a primary focus on establishing a foundational framework that integrates various nonlinear SDR methods. We illustrate the generalized sliced inverse regression (GSIR) and the generalized sliced average variance estimation (GSAVE) which are fitted by the framework. Further, we delve into nonlinear extensions of inverse moments through the kernel trick, specifically examining the kernel sliced inverse regression (KSIR) and kernel canonical correlation analysis (KCCA), and explore their relationships within the established framework. We also briefly explain the nonlinear SDR for functional data. In addition, we present practical aspects such as algorithmic implementations. This paper concludes with remarks on the dimensionality problem of the target function class.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.3
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• pp.281-301
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• 2014

This study aims to understand the characteristics of civil petitions and complaints within public libraries and to identify factors to the civil petitions. We develop a survey questionnaire for the goals based on the previous related studies and a pilot study with several open-ended interviews with public library users. Then we collect data using the questionnaire from 154 survey participants (public library users). The collected data are statistically analyzed by using factor analysis, canonical correlation analysis, and multiple linear regression analysis. The results show that there are two identified dependent factors, such as petition and complaint, and six independent factors, including librarian specialty, service convenience, and communication between librarians and users. Through a series of statistical analysis for identifying specific relationships between individual dependent factors and predictors, we discuss the characteristics of the library civil petitions and complaints, and the importance of a structural/relation-based approach to the library civil petition as a complimentary for the functional approach. Based on the results and discussions, we suggest several future research directions, including research on the relationship between the library civil petitions and library performances.