The underlying idea of Reich, Fuentes, and Dunson (2009) to use the asymptotic normal approximation of the quantile regression estimator as a “substitute” likelihood can be regarded as a convenient dumbing-down of the Jeffrey’s idea elaborated by Lavine (1995) and Dunson and Taylor (2005). The obvious disadvantage of the original Jeffrey’s suggestion is that it is difficult to compute/update th...

BACKGROUND The objective of this study was to determine the relationship between the quality of feeding practices and children's nutritional status in rural western China. METHODS A sample of 12,146 pairs of 6- to 35-month-old children and their mothers were recruited using stratified multistage cluster random sampling in rural western China. Quantile regression was used to analyze the relati...

An appealing way of working with probability distributions, especially in nonparametric inference, is through “descriptive measures” that characterize features of particular interest. One attractive approach is to base the measures on quantiles. Here we consider the multivariate context and utilize the “spatial quantiles”, a recent vector extension of univariate quantiles that is becoming incre...

Asymptotic distribution Asymptotic distribution Asymptotic distribution Asymptotic distribution of two of two of two of two-sample empirical sample empirical sample empirical sample empirical U U U U-quantiles with application quantiles with application quantiles with application quantiles with applications s s s to robust tests for structural to robust tests for structural to robust tests for ...

[3] Rakesh Agrawal and Arun Swami. A one-pass space-efficient algorithm for finding quantiles. A one-pass algorithm for accurately estimating quantiles for disk-resident data. [8] Jürgen Beringer and Eyke Hüllermeier. An efficient algorithm for instance-based learning on data streams.

In this paper we will study the relationship between the number of replications and the accuracy of the estimated quantiles of a distribution obtained by simulation. A method for testing hypotheses on the quantiles of a theoretical distribution using the simulated distribution is proposed, as well as a method to check the hypothesis of consistency of a test.

We address the estimation of “extreme” conditional quantiles i.e. when their order converges to one as the sample size increases. Conditions on the rate of convergence of their order to one are provided to obtain asymptotically Gaussian distributed kernel estimators. A Weissman-type estimator and kernel estimators of the conditional tailindex are derived, permitting to estimate extreme conditio...