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Functional kernel estimators of large conditional quantiles
Conditional quantiles heavy-tailed distributions functional kernel estimator
2011/7/19
We address the estimation of conditional quantiles when the covariate is functional and when the order of the quantiles converges to one as the sample size increases.
Estimating conditional quantiles with the help of the pinball loss
nonparametric regression quantile estimation support vector machines
2011/3/21
The so-called pinball loss for estimating conditional quantiles is a well-known tool in both statistics and machine learning. So far, however, only little work has been done to quantify the efficiency...
Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth
Multivariate quantile quantile regression halfspace depth
2010/3/10
A new multivariate concept of quantile, based on a directional
version of Koenker and Bassett’s traditional regression quantiles, is
introduced for multivariate location and multiple-output regressi...
Discussion of “Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth”
Multivariate quantiles multiple-output regression quantiles L1 optimization halfspace depth
2010/3/10
First I would like to congratulate the authors for developing a new concept
of directional quantile contours. The work will contribute well to the pursuit
of multivariate quantiles. The multiple out...
The distribution and quantiles of functionals of weighted empirical distributions when observations have different distributions
Edgeworth-Cornish-Fisher expansions von Mises derivatives Weighted em-pirical distribution
2010/3/10
This paper extends Edgeworth-Cornish-Fisher expansions for the distribution
and quantiles of nonparametric estimates in two ways. Firstly it allows observations to have
different distributions. Seco...
Two approaches to constructing simultaneous confidence bounds for quantiles
Two approaches constructing simultaneous confidence bounds quantiles
2009/9/24
Two approaches to constructing simultaneous confidence bounds for quantiles。
Bahadur's representation of sample quantiles based on smoothed estimates of a distribution function
Bahadur's representation of sample quantiles smoothed estimates of a distribution function
2009/9/24
Bahadur's representation of sample quantiles based on smoothed estimates of a distribution function。
Expansions for Quantiles and Multivariate Moments of Extremes for Distributions of Pareto Type
Bell polynomials Extremes Inversion theorem Moments Pareto Quantiles
2010/3/19
Let Xnr be the rth largest of a random sample of size n from a distribution
F(x) = 1 −P∞i=0 cix− −i for > 0 and > 0. An inversion theorem is proved and used to
derive an expan...
Bahadur representation of sample quantiles for functional of Gaussian dependent sequences under a minimal assumption
Gaussian processes Bahadur representation of sample quantiles Hermiteexpansions
2010/4/26
We obtain a Bahadur representation for sample quantiles of nonlinear functional
of Gaussian sequences with correlation function decreasing as k− for
some > 0. This representation is derived...