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  • The restrictive properties of compositional data, that is multivariate data with positive parts that carry only relative information in their components, call for special care to be taken while performing standard statistical methods, for example, regression analysis. Among the special methods suitable for handling this problem is the total least squares procedure (TLS, orthogonal regression, regression with errors in variables, calibration problem), performed after an appropriate log-ratio transformation. The difficulty or even impossibility of deeper statistical analysis (confidence regions, hypotheses testing) using the standard TLS techniques can be overcome by calibration solution based on linear regression. This approach can be combined with standard statistical inference, for example, confidence and prediction regions and bounds, hypotheses testing, etc., suitable for interpretation of results.
  • The restrictive properties of compositional data, that is multivariate data with positive parts that carry only relative information in their components, call for special care to be taken while performing standard statistical methods, for example, regression analysis. Among the special methods suitable for handling this problem is the total least squares procedure (TLS, orthogonal regression, regression with errors in variables, calibration problem), performed after an appropriate log-ratio transformation. The difficulty or even impossibility of deeper statistical analysis (confidence regions, hypotheses testing) using the standard TLS techniques can be overcome by calibration solution based on linear regression. This approach can be combined with standard statistical inference, for example, confidence and prediction regions and bounds, hypotheses testing, etc., suitable for interpretation of results. (en)
Title
  • Total least squares solution for compositional data using linear models
  • Total least squares solution for compositional data using linear models (en)
skos:prefLabel
  • Total least squares solution for compositional data using linear models
  • Total least squares solution for compositional data using linear models (en)
skos:notation
  • RIV/61989592:15310/10:10212158!RIV11-MSM-15310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6198959214)
http://linked.open...iv/cisloPeriodika
  • 7
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 293084
http://linked.open...ai/riv/idVysledku
  • RIV/61989592:15310/10:10212158
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Total least squares; Multivariate outliers; Linear regression model; Isometric log-ratio transformation; Estimation; Confidence ellipse; Calibration line (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV
  • [75C7B66C704B]
http://linked.open...i/riv/nazevZdroje
  • Journal of Applied Statistics
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 37
http://linked.open...iv/tvurceVysledku
  • Hron, Karel
  • Fišerová, Eva
http://linked.open...ain/vavai/riv/wos
  • 000279209700005
http://linked.open...n/vavai/riv/zamer
issn
  • 0266-4763
number of pages
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  • 15310
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