• Communications for Statistical Applications and Methods
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• 제25권4호
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• pp.385-396
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• 2018

Competing risks are commonly encountered in biomedical research. Regression models for competing risks data can be developed based on data routinely collected in hospitals or general practices. However, these data sets usually contain the covariate missing values. To overcome this problem, multiple imputation is often used to fit regression models under a MAR assumption. Here, we introduce a multivariate imputation in a chained equations algorithm to deal with competing risks survival data. Using pseudo-observations, we make use of the available outcome information by accommodating the competing risk structure. Lastly, we illustrate the practical advantages of our approach using simulations and two data examples from a coronary artery disease data and hepatocellular carcinoma data.

• Journal of the Korean Statistical Society
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• 제19권1호
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• pp.24-44
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• 1990

The purpose of this paper is to extend the idea of Tamhane and Bechhofer (1977, 1979) concerning the normal means problem to some general class of distributions. The key idea in Tamhane and Bechhofer is the derivation of the computable lower bounds on the probability of a correct selection. To derive such lower bounds, they used the specific covariance structure of a multivariate normal distribution. It is shown that such lower bounds can be obtained for a class of stochastically increasing distributions under certain conditions, which is sufficiently general so as to include the normal means problem as a special application. As an application of the general theory to the scale parameters problem, a two-stage elimination type procedure for selecting the population associated with the smallest variance from among several normal populations is proposed. The design constants are tabulated and the relative efficiencies are computed.

• Environmental Analysis Health and Toxicology
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• 제23권4호
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• pp.231-245
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• 2008

Various methods for detecting changes in response behaviors of indicator specimens are presented for monitoring effects of toxic treatments. The movement patterns of individuals are quantitatively characterized by statistical (i.e., ANOVA, multivariate analysis) and computational (i.e., fractal dimension, Fourier transform) methods. Extraction of information in complex behavioral data is further illustrated by techniques in ecological informatics. Multi-Layer Perceptron and Self-Organizing Map are applied for detection and patterning of response behaviors of indicator specimens. The recent techniques of Wavelet analysis and line detection by Recurrent Self-Organizing Map are additionally discussed as an efficient tool for checking time-series movement data. Behavioral monitoring could be established as new methodology in integrative ecological assessment, tilling the gap between large-scale (e.g., community structure) and small-scale (e.g., molecular response) measurements.

• 정보화연구
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• 제11권2호
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• pp.125-130
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• 2014

Today in different higher educational institutions there is the active on a structure of expert models of monitoring the quality of education in the higher educational institutions, assuming continuous tracking an education status as a whole and its separate components. In most cases creation of a monitoring system of quality of education relies on the intermediate results of activities.

• 응용통계연구
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• 제11권2호
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• pp.389-401
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• 1998

본 논문에서는 멀티미디어를 활용한 회귀분석 학습시스템 CybeRClass(Cyber Regression Class)를 소개하고자 한다. CybeRClass는 음성정보와 애니메이션 등을 활용하여 회귀분석에 대한 학습을 시켜주는 시스템이다. 이 시스템은 군집분석이나 판별분석 등의 다변량분석 학습이 가능하도록 설계되었다. 멀티미디어 기술을 위한 도구로는 Multimedia ToolBook을 사용하였으며, 통계계산과 통계그라픽을 위해서는 객체지향 통계 언어인 Xlisp-Stat을 사용하였다.

• Communications for Statistical Applications and Methods
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• 제17권2호
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• pp.153-164
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• 2010

본 논문에서는 이산형과 연속형 데이터가 혼합되어 있는 데이터 구조를 탐색하기 위하여 동적 기법을 사용하였다. 이산형 변수들간의 관계를 표현하는 선 모자이크 도표와 연속형 변수들의 관계를 위한 산점도, 일변량 변수 관점에서의 데이터의 분포를 파악할 수 있는 상자도표를 동시에 사용하면서, 동적인 기법들을 적용하여 다차원 데이터에 대한 구조를 좀 더 쉽게 파악할 수 있음을 보였다.

• 한국멀티미디어학회논문지
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• 제12권12호
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• pp.1755-1760
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• 2009

In this research, for the first time, we tried to analyse Raman hyperspectral dentin data using Independent Component Analysis (ICA) to see its possibility of adoption for the dental analysis software. We captured hyperspectral dentin data on 569 spots on a molar with dental lesion by HR800 Micro Raman Spectrometer at UMKC-CRISP (University of Missouri at Kansas City-Center for Research on Interfacial Structure and Properties). Each spot has 1,005 hyperspectral data. We applied ICA to the captured hyperspectral data of dentin for evaluating ICA approach, and compared it with the well known multivariate analysis method, PCA. As a result of the experiment, ICA approach shows better local characteristic of dentin than the result of PCA. We confirmed that ICA also could be a good method along with PCA in the dental analysis software.

• 한국BIM학회 논문집
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• 제9권1호
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• pp.22-31
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• 2019

In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.

• Genomics & Informatics
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• 제17권1호
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• pp.6.1-6.9
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• 2019

Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. The development of high-throughput sequencing technologies has boosted microbiome research through the study of microbial genomes and allowing a more precise quantification of microbiome abundances and function. Microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional nature. In this review we outline some of the procedures that are most commonly used for microbiome analysis and that are implemented in R packages. We place particular emphasis on the compositional structure of microbiome data. We describe the principles of compositional data analysis and distinguish between standard methods and those that fit into compositional data analysis.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.289-294
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• 1998

The distributed-parameter structures expressed with the partial differential equations are considered as the infinite-dimensional dynamic system. For implementation of a controller in multivariate systems, it is necessary to derive the state-space reduced order model. By the eigensystem realization algorithm, we can yield tile subspace system with the Markov parameters derived from the measured frequency response function by the inverse discrete Fourier transformation. We also review the necessary conditions for the convergence of the approximation system and the error bounds in terms of the singular values of Markov-parameter matrices. To determine the natural frequencies and modal damping ratios, the modal coordinate transformation is applied to the realization system. The vibration test for a smart structure is performed to provide the records of frequency response functions used in the subspace system realization.