Statistical inference in orthogonal regression for three-part compositional data using a linear model with type-II constraints

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F12%3A33140420" target="_blank" >RIV/61989592:15310/12:33140420 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1080/03610926.2011.604145" target="_blank" >http://dx.doi.org/10.1080/03610926.2011.604145</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03610926.2011.604145" target="_blank" >10.1080/03610926.2011.604145</a>

Result language
angličtina
Original language name
Statistical inference in orthogonal regression for three-part compositional data using a linear model with type-II constraints
Original language description
Orthogonal regression is a proper tool to analyze relations between two variables when three-part compositional data, i.e. three-part observations carrying relative information (like proportions or percentages), are under examination. When linear statistical models with type-II constraints (constraints involving other parameters besides the ones of the unknown model) are employed for estimating the parameters of the regression line, approximate variances and covariances of the estimated line coefficients can be determined. Moreover, additional assumption of normality enables to construct confidence domains and perform hypotheses testing. The theoretical results are applied to real-world examples.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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