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On multivariate quantiles under partial orders
This paper focuses on generalizing quantiles from the ordering point of view.
We propose the concept of partial quantiles, which are based on a given partial
order. We establish that partial quantiles are equivariant under
order-preserving transformations of the data, robust to outliers, characterize
the probability distribution if the partial order is sufficiently rich,
generalize the concept of efficient frontier, and can measure dispersion from
the partial order perspective. We also study several statistical aspects of
partial quantiles. We provide estimators, associated rates of convergence, and
asymptotic distributions that hold uniformly over a continuum of quantile
indices. Furthermore, we provide procedures that can restore monotonicity
properties that might have been disturbed by estimation error, establish
computational complexity bounds, and point out a concentration of measure
phenomenon (the latter under independence and the componentwise natural order).
Finally, we illustrate the concepts by discussing several theoretical examples
and simulations. Empirical applications to compare intake nutrients within
diets, to evaluate the performance of investment funds, and to study the impact
of policies on tobacco awareness are also presented to illustrate the concepts
and their use.Comment: Published in at http://dx.doi.org/10.1214/10-AOS863 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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