Asymptotic theorems for kernel U-quantiles

Limit theorems for functions of marginal quantiles

G. JOGESH BABU1, ZHIDONG BAI2,* , KWOK PUI CHOI2,** and VASUDEVAN MANGALAM3 1Department of Statistics, 326 Joab L. Thomas Building, The Pennsylvania State University, University Park, PA 16802-2111, USA. E-mail: [email protected] 2Department of Statistics and Applied Probability, National University of Singapore, 6 Science Drive 2, Singapore 117546. E-mail: *[email protected]; **[email protected]...

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

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 ...

Uniqueness theorems for kernel methods

This paper has three goals: to find uniqueness theorems for some well-known kernel methods, and thereby to better understand their behavior; to derive theorems which apply to more general families of kernel methods; and to collect results which are hoped to be useful to workers who would like to prove uniqueness theorems for their own algorithms. Knowing when a solution is unique has the obviou...

Functional kernel estimators of large conditional quantiles

Functional kernel estimators of conditional extreme quantiles

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...