Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 15, Issue 5, Pages 384-398Publisher
SPRINGER
DOI: 10.1007/s004770100077
Keywords
Aitchison geometry; compositional data; Euclidean space; finite dimensional Hilbert space; metric center; metric variance
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The geometric interpretation of the expected value and the variance in real Euclidean space is used as a starting point to introduce metric counterparts on an arbitrary finite dimensional Hilbert space. This approach allows us to define general reasonable properties for estimators of parameters, like metric unbiasedness and minimum metric variance, resulting in a useful tool to better understand the logratio approach to the statistical analysis of compositional data, who's natural sample space is the simplex.
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