• Journal of Preventive Medicine and Public Health
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• 제31권4호
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• pp.875-884
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• 1998

Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the EM algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which uses only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2005년도 추계학술대회 발표논문집
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• pp.159-167
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• 2005

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.

• Journal of Nutrition and Health
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• 제45권6호
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• pp.600-612
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• 2012

Nutritional researchers world-wide are using large-scale sample survey methods to study nutritional health epidemiology and services utilization in general, non-clinical populations. This article provides a review of important statistical methods and software that apply to descriptive and multivariate analysis of data collected in sample surveys, such as national health and nutrition examination survey. A comparative data analysis of the Korea National Health and Nutrition Examination Survey (KNHANES) was used to illustrate analytical procedures and design effects for survey estimates of population statistics, model parameters, and test statistics. This article focused on the following points, method of approach to analyze of the sample survey data, right software tools available to perform these analyses, and correct survey analysis methods important to interpretation of survey data. It addresses the question of approaches to analysis of complex sample survey data. The latest developments in software tools for analysis of complex sample survey data are covered, and empirical examples are presented that illustrate the impact of survey sample design effects on the parameter estimates, test statistics, and significance probabilities (p values) for univariate and multivariate analyses.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.406-408
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• 2006

Various geological thematic maps such as grain size or ground water level maps have been generated by interpolating sparsely sampled ground survey data. When there are sampled data at a limited number of locations, to use secondary information which is correlated to primary variable can help us to estimate the attribute values of the primary variable at unsampled locations. This paper applies two multivariate geostatistical algorithms to integrate remote sensing imagery with sparsely sampled ground survey data for spatial estimation of grain size: simple kriging with local means and kriging with an external drift. High-resolution IKONOS imagery which is well correlated with the grain size is used as secondary information. The algorithms are evaluated from a case study with grain size observations measured at 53 locations in the Baramarae beach of Anmyeondo, Korea. Cross validation based on a one-leave-out approach is used to compare the estimation performance of the two multivariate geostatistical algorithms with that of traditional ordinary kriging.

• 정보보호학회논문지
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• 제33권1호
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• pp.1-12
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• 2023

다변량 시계열 이상 탐지 과업에서 정답 값이 존재하는 데이터를 얻는 것은 매우 시간 집약적인 일이다. 따라서 최근 정답 값이 필요 없는 비지도 학습법(unsupervised learning)에 관한 많은 연구가 진행되었다. 하지만 다변량 시계열 이상 탐지 과업에 특화된 주요 구조와 세부적인 특성에 대한 심화 있는 논의는 이루어지지 않았다. 본 논문에서는 비지도 학습 기반의 다변량 시계열 이상 탐지 모델과 특장점을 포괄적으로 분석하여 분류하였다. 전력 계통(power grid) 또는 Cyber Physical System(CPS)과 같은 현실 세계 데이터 집합에서 현실적인 이상 상황을 고려하여 학습을 진행하였고, 실험 결과를 바탕으로 각 모델의 정량적 성능을 비교 분석하였다. 성능 지표로는 정밀도(precision), 재현율(recall)과 F1 점수를 사용하여 성능을 측정하였다.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.755-760
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• 1999

In multivariate stratified sampling surveys it is general to use a few stratification variables which are highly correlated with the important variables at design stage. But there might be some secondary study variables which are not so highly correlated with those stratification variables. In that case it is not efficient to use the same type of estimator due to the secondary variables as the one base on the important variables. A post-stratified estimation is proposed to increase the efficiency of the estimator with existence of secondary variables. The proposed method is illustrated with a set of fishery household population survey data.

• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.27-43
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• 2024

This study investigated technological and managerial barriers in technology startups through a survey of 151 companies, yielding 118 responses (78.1% response rate). Factor and multivariate analyses identified two distinct barriers: technological and managerial. Reliability analysis validated the measurement tool. Using MANCOVA, 12 hypotheses were tested, incorporating six independent variables. Results revealed significant disparities in technological and managerial barriers based on establishment type, commercialization goals, growth stage, and commercialization stage, with 5 hypotheses supported. This study highlights the crucial role of these variables in understanding barriers within technology-based startups.

• Communications for Statistical Applications and Methods
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• 제20권2호
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• pp.97-105
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• 2013

Stratification (among other applications) is a popular technique used in survey practice to improve the accuracy of estimators. Its full potential benefit can be gained by the effective use of auxiliary variables in stratification related to survey variables. This paper focuses on the problem of stratum formation when multiple stratification variables are available. We first review a variance reduction strategy in the case of univariate stratification. We then discuss its use for multivariate situations in convenient and efficient ways using three methods: compromised measures of size, principal components analysis and a K-means clustering algorithm. We also consider three types of compromising factors to data when using these three methods. Finally, we compare their efficiency using data from MU281 Swedish municipality population.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2005년도 추계학술대회 발표논문집
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• pp.169-183
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• 2005

가중치 조정(weighting adjustment)으로 단위 무응답(unit nonresponse)을 처리하는 문제에서 성향점수를 추정하는 모형을 만들기 위해 응답변수와 관심변수를 동시에 고려하는 다변량 회귀나무(multivariate regression tree)기법을 제안하였다. 효과적인 무응답 조정층 구축을 위해 응답한 개체들만 사용하는 경우와 모든 개체들을 사용하는 경우를 제시하고 이 두방법을 편향의 관점으로 비교한다.