Multiple Adjusted Quantiles

Adjusted Empirical Likelihood Method for Quantiles

Multivariate Quantiles and Multiple-output Regression Quantiles: from L1 Optimization to Halfspace Depth

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 regression problems. In their empirical version, those quantiles can be computed efficiently via linear programming techniques. Consistency, Bahadur representation and asymptotic normality results are establ...

Computing Multiple-Output Regression Quantile Regions from Projection Quantiles

In the multiple-output regression context, Hallin, Paindaveine and Šiman (2010) introduced a powerful data-analytical tool based on regression quantile regions. However, the computation of these regions, that are obtained by considering in all directions an original concept of directional regression quantiles, is a very challenging problem. Paindaveine and Šiman (2010b) described a first elegan...

Spatial Scan Statistics Adjusted for Multiple Clusters

The spatial scan statistic is one of the main epidemiological tools to test for the presence of disease clusters in a geographical region. While the statistical significance of the most likely cluster is correctly assessed using the model assumptions, secondary clusters tend to have conservatively high P-values. In this paper, we propose a sequential version of the spatial scan statistic to adj...

Regularized simultaneous model selection in multiple quantiles regression

Simultaneously estimating multiple conditional quantiles is often regarded as a more appropriate regression tool than the usual conditional mean regression for exploring the stochastic relationship between the response and covariates. When multiple quantile regressions are considered, it is of great importance to share strength among them. In this paper, we propose a novel regularization method...