• Title/Summary/Keyword: multivariate analysis

Search Result 3,108, Processing Time 0.025 seconds

Hydrological homogeneous region delineation for bivariate frequency analysis of extreme rainfalls in Korea (다변량 L-moment를 이용한 이변량 강우빈도해석에서 수문학적 동질지역 선정)

  • Shin, Ju-Young;Jeong, Changsam;Joo, Kyungwon;Heo, Jun-Haeng
    • Journal of Korea Water Resources Association
    • /
    • v.51 no.1
    • /
    • pp.49-60
    • /
    • 2018
  • The multivariate regional frequency analysis has many advantages such as an adaption of regional parameters and consideration of a correlated structure of the data. The multivariate regional frequency analysis can provide the broader and more detailed information for the hydrological variables. The multivariate regional frequency analysis has not been attempted to model hydrological variables in South Korea yet. Therefore, it is required to investigate the applicability of the multivariate regional frequency analysis in the modeling of the hydrological variables. The current study investigated the applicability of the homogeneous region delineation and their characteristics in bivariate regional frequency analysis of annual maximum rainfall depth-duration data. The K-medoid method was employed as a clustering method. The discordancy and heterogeneous measures were used to assess the appropriateness of the delineation results. According to the results of the clustering analysis, the employed stations could be grouped into five regions. All stations at three of the five regions led to acceptable values of discordancy measures than the threshold. The stations where have short record length led to the large discordancy measures. All grouped regions were identified as a homogeneous region based on heterogeneous measure estimates. It was observed that there are strong cross-correlations among the stations in the same region.

Residuals Plots for Repeated Measures Data

  • PARK TAESUNG
    • Proceedings of the Korean Statistical Society Conference
    • /
    • 2000.11a
    • /
    • pp.187-191
    • /
    • 2000
  • In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

  • PDF

Statistical Outliers in Florida Counties at the Presidential Election 2000 (2000년 미국대선 플로리다주의 투표결과 분석)

  • 김현철
    • The Korean Journal of Applied Statistics
    • /
    • v.15 no.1
    • /
    • pp.21-32
    • /
    • 2002
  • We searched out in the votes data of the State of Florida at presidential election 2000. We used a multivariate regression analysis. We got there were several outliers including Palm Beach County. It means that we should analyze the number of disqualified ballots which were double-punched as well as the votes, to insist the " Butterfly Ballot" made Palm Beach outlier.

Diagnosis of Observations after Fit of Multivariate Skew t-Distribution: Identification of Outliers and Edge Observations from Asymmetric Data

  • Kim, Seung-Gu
    • The Korean Journal of Applied Statistics
    • /
    • v.25 no.6
    • /
    • pp.1019-1026
    • /
    • 2012
  • This paper presents a method for the identification of "edge observations" located on a boundary area constructed by a truncation variable as well as for the identification of outliers and the after fit of multivariate skew $t$-distribution(MST) to asymmetric data. The detection of edge observation is important in data analysis because it provides information on a certain critical area in observation space. The proposed method is applied to an Australian Institute of Sport(AIS) dataset that is well known for asymmetry in data space.

A Study on the Distribution Specific Characteristics about Each Group of Exhibition Space on Museum through Multivariate Analysis - Focused on Establishment of Quantitative Analysis Characteristics and Main Component Analysis - (다변량해석에 의한 박물관 전시공간의 그룹별 분포특성 - 정량적 분석지표의 설정과 주성분분석을 중심으로 -)

  • Park Moo-Ho;Cho Jae-Wook;Lim Che-Zinn
    • Korean Institute of Interior Design Journal
    • /
    • v.13 no.6
    • /
    • pp.132-139
    • /
    • 2004
  • This study is to a question in argument that existing theses about a trait spatial configuration of exhibition space were analyzed without appropriateness verification of analysis characteristics. Firstly, through theoretical studies of established thesis, validity twenty analysis characteristics was chosen by making an investigation into existing analysis characteristics. Secondly, through a subject of our investigation, forty-two exhibition space of nineteen museums and art museum at home and abroad, a distribution map of exhibition space was analyzed by multivariate analysis. As a result of this study : 1) Nine analysis characteristics which extracted through multivariate analysis was the principal analysis characteristics. 2) A scale was important characteristic for the classification of museum therefore a degree of space perception was ought to compare every one of similar scale museum. 3) When comparing a trait of spatial configuration at exhibition space, these characteristics came into effect on middle sized museums. 4) It was visually confirmed a trait of spatial configuration of each group between museum and art museum

Assessment of Water Quality using Multivariate Statistical Techniques: A Case Study of the Nakdong River Basin, Korea

  • Park, Seongmook;Kazama, Futaba;Lee, Shunhwa
    • Environmental Engineering Research
    • /
    • v.19 no.3
    • /
    • pp.197-203
    • /
    • 2014
  • This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

Detection of Hotspots on Multivariate Spatial Data

  • Moon, Sung-Ho
    • Journal of the Korean Data and Information Science Society
    • /
    • v.17 no.4
    • /
    • pp.1181-1190
    • /
    • 2006
  • Statistical analyses for spatial data are important features for various types of fields. Spatial data are taken at specific locations or within specific regions and their relative positions are recorded. Lattice data are synoptic observation covering an entire spatial region, like cancer rates corresponding to each county in a state. Until now, the echelon analysis has been applied only to univariate spatial data. As a result, it is impossible to detect the hotspots on the multivariate spatial data In this paper, we expand the spatial data to time series structure. And then we analyze them on the time space and detect the hotspots. Echelon dendrogram has been made by piling up each multivariate spatial data to bring time spatial data. We perform the structural analysis of temporal spatial data.

  • PDF

A Multivariate Statistical Approach to the Categorization of Body Types for Korean Adults (다변량 통계분석 방법을 이용한 한국인 성인 남녀 체형분류)

  • Seong, Deok-Hyun;Jung, Eui-S.
    • Journal of the Ergonomics Society of Korea
    • /
    • v.24 no.4
    • /
    • pp.39-46
    • /
    • 2005
  • The purpose of the study is to suggest a methodology for properly categorizing the body type of Koreans based on the multivariate statistical analysis. Anthropometric data used in the study were measured from the sampled strata of about fifteen thousand Koreans surveyed through the 5th national anthropometic data measurement project called Size Korea funded by ATS, Korea, during 2003-2004. In order to categorize whole body types, the normalized anthropometric variables, being divided by its stature, were used for obtaining a set of factors that supposedly represent body types through the factor analysis. These factors, which were again clustered, yielded the body types according to the gender. The body types classified are expected to be applied to product design for clothing, furniture, automobile packaging, etc.

A Study of Simple Rock Mass Rating for Tunnel Using Multivariate Analysis (다변량분석을 이용한 터널에서의 간편 RMR에 관한 연구)

  • 위용곤;노상림;윤지선
    • Proceedings of the Korean Geotechical Society Conference
    • /
    • 2000.11a
    • /
    • pp.493-500
    • /
    • 2000
  • Rock Mass Rating has been widely applied to the underground tunnel excavation and many other practical problems in rock engineering. However, Rock Mass Rating is hard to make out because it is difficult to estimate each valuation items through all kind of field situations and items of RMR have interdependence. So the experts of tunnel assessment have problems with rating rock mass. In this study, using multivariate analysis based on domestic data(1011EA) of water conveyance tunnel, we presented rock mass rating system which is objective and easy to use. The constituents of RMR are decided to RQD, condition of discontinuities, groundwater conditions, orientation of discontinuities, intact rock strength, spacing of discontinuities in important order. In each step, we proposed the best multiple regression model for RMR system. And using data which have been collected at other site, we examined that presented multiple regression model was useful.

  • PDF

A Study on the Stability Evaluation of Railway Cut-Slope Under Rainfall (강우시 철도 절개사면의 안정성 평가에 관한 연구)

  • 김현기;박영곤;신민호
    • Proceedings of the Korean Geotechical Society Conference
    • /
    • 2001.03a
    • /
    • pp.273-280
    • /
    • 2001
  • In order to evaluate the stability of railway cut-slope under rainfall, explanatory variables and subordinate variables were selected for multivariate analysis. Furthermore the site which had occurred failure due to rainfall was investigated, and by executing multivariate analysis for 121 cases, critical rainfall was defined by the case that had high value of correlation factor. The 0.3 square value of maximum hourly rainfall during 24 hours before failure caused the collapse of railway cut-slope and could be used to estimate the stability of railway cut-slope. From the result of application to a collapse example, the evaluaton method by critical rainfall curve is satisfactory.

  • PDF