activity
20072012
most citedThresholding methods to estimate the copula density

54 citations · 61 across the 5 of their papers we have counts for

collaborators

6 papers

math.ST2012

Grouping Strategies and Thresholding for High Dimensional Linear Models

Mathilde Mougeot, Dominique Picard, Karine Tribouley

The estimation problem in a high regression model with structured sparsity is investigated. An algorithm using a two steps block thresholding procedure called GR-LOL is provided. C…

math.ST2011★ 2 cited

A new selection method for high-dimensionial instrumental setting: application to the Growth Rate convergence hypothesis

Mathilde Mougeot, Dominique Picard, Karine Tribouley

This paper investigates the problem of selecting variables in regression-type models for an "instrumental" setting. Our study is motivated by empirically verifying the conditional…

math.ST2010

Learning Out of Leaders

Mathilde Mougeot, Dominique Picard, Karine Tribouley

This paper investigates the estimation problem in a regression-type model. To be able to deal with potential high dimensions, we provide a procedure called LOL, for Learning Out of…

math.ST2009★ 3 cited

A test of goodness-of-fit for the copula densities

Ghislaine Gayraud, Karine Tribouley

We consider the problem of testing hypotheses on the copula density from bi-dimensional observations. We wish to test the null hypothesis characterized by a parametric class ag…

math.ST2008★ 54 cited

Thresholding methods to estimate the copula density

Florent Autin, Erwan Le Pennec, Karine Tribouley

This paper deals with the problem of the multivariate copula density estimation. Using wavelet methods we provide two shrinkage procedures based on thresholding rules for which the…

math.ST2007★ 2 cited

Functional approach for excess mass estimation in the density model

Cristina Butucea, Mathilde Mougeot, Karine Tribouley

We consider a multivariate density model where we estimate the excess mass of the unknown probability density at a given level from i.i.d. observed random variables.…