Abstract
The second part is devoted to the analysis of several conditional dependence models. We propose some tests of the simplifying assumption that a conditional copula is constant with respect to its conditioning event, and prove the consistency of a semiparametric bootstrap scheme.
If the conditional copula is not constant with respect to the conditional event, then it can be modelled using the corresponding Kendall's tau. We study the estimation of this conditional dependence parameter using 3 different approaches : kernel techniques, regressiontype models and classification algorithms.
The last part regroups two different topics in inference. We review and propose estimators for regular conditional functionals using Ustatistics. Finally, we study the construction and the theoretical properties of confidence intervals for ratios of means under different sets of assumptions and paradigms.
Original language  English 

Awarding Institution 

Supervisors/Advisors 

Award date  15 May 2019 
Publication status  Published  2019 
Externally published  Yes 
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Keywords
 Conditional copula
 highdimensional statistics
 conditional distribution
Cite this
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Some statistical results in highdimensional dependence modeling. / Derumigny, Alexis.
2019. 304 p.Research output: Thesis › PhD Thesis  Research external, graduation external › Academic
TY  THES
T1  Some statistical results in highdimensional dependence modeling
AU  Derumigny, Alexis
PY  2019
Y1  2019
N2  This thesis can be divided into three parts. In the first part, we study adaptivity to the noise level in the highdimensional linear regression framework. We prove that two squareroot estimators attains the minimax rates of estimation and prediction. We show that a corresponding medianofmeans version can still attains the same optimal rates while being robust to outliers in the data.The second part is devoted to the analysis of several conditional dependence models. We propose some tests of the simplifying assumption that a conditional copula is constant with respect to its conditioning event, and prove the consistency of a semiparametric bootstrap scheme.If the conditional copula is not constant with respect to the conditional event, then it can be modelled using the corresponding Kendall's tau. We study the estimation of this conditional dependence parameter using 3 different approaches : kernel techniques, regressiontype models and classification algorithms.The last part regroups two different topics in inference. We review and propose estimators for regular conditional functionals using Ustatistics. Finally, we study the construction and the theoretical properties of confidence intervals for ratios of means under different sets of assumptions and paradigms.
AB  This thesis can be divided into three parts. In the first part, we study adaptivity to the noise level in the highdimensional linear regression framework. We prove that two squareroot estimators attains the minimax rates of estimation and prediction. We show that a corresponding medianofmeans version can still attains the same optimal rates while being robust to outliers in the data.The second part is devoted to the analysis of several conditional dependence models. We propose some tests of the simplifying assumption that a conditional copula is constant with respect to its conditioning event, and prove the consistency of a semiparametric bootstrap scheme.If the conditional copula is not constant with respect to the conditional event, then it can be modelled using the corresponding Kendall's tau. We study the estimation of this conditional dependence parameter using 3 different approaches : kernel techniques, regressiontype models and classification algorithms.The last part regroups two different topics in inference. We review and propose estimators for regular conditional functionals using Ustatistics. Finally, we study the construction and the theoretical properties of confidence intervals for ratios of means under different sets of assumptions and paradigms.
KW  Conditional copula
KW  highdimensional statistics
KW  conditional distribution
M3  PhD Thesis  Research external, graduation external
ER 