Self-normalized processes are of common occurrence in probabilistic and statistical studies. This volume covers developments in the area, including self-normalized large and moderate deviations, and laws of the iterated logarithms for self-normalized martingales. It treats the theory and applications of self-normalization
We use the martingale approach to study large deviations and laws of the iterated logarithm for cert...
Cramér type moderate deviation theorems quantify the accuracy of the relative error of the normal ap...
Stein's method is a powerful tool in estimating accuracy of various probability approximations. It w...
Abstract: Self-normalized processes are basic to many probabilistic and statistical studies. They ar...
AbstractMultivariate self-normalized processes, for which self-normalization consists of multiplying...
We establish asymptotic distribution results for self-normalized Lévy processes at small and large t...
National audienceWe prove, for martingales self-normalized by their increasing process, the upper bo...
Let X-1,X-2, ... be a sequence of independent random variables (r.v.s) belonging to the domain of at...
Let be i.i.d. -valued random variables. We prove partial moderate deviation principles for self-norm...
National audienceWe prove a self-normalized large deviation principle for sums of Banach space value...
We propose several exponential inequalities for self-normalized martingales similar to those establi...
International audienceWe propose new concentration inequalities for self-normalized martingales. The...
Cram\'er's moderate deviations give a quantitative estimate for the relative error of the normal app...
AbstractLet {Xn,n⩾1} be i.i.d. Rd-valued random variables. We prove partial moderate deviation princ...
We prove a self normalized central limit theorem for a new mixing class of processes introduced in K...
We use the martingale approach to study large deviations and laws of the iterated logarithm for cert...
Cramér type moderate deviation theorems quantify the accuracy of the relative error of the normal ap...
Stein's method is a powerful tool in estimating accuracy of various probability approximations. It w...
Abstract: Self-normalized processes are basic to many probabilistic and statistical studies. They ar...
AbstractMultivariate self-normalized processes, for which self-normalization consists of multiplying...
We establish asymptotic distribution results for self-normalized Lévy processes at small and large t...
National audienceWe prove, for martingales self-normalized by their increasing process, the upper bo...
Let X-1,X-2, ... be a sequence of independent random variables (r.v.s) belonging to the domain of at...
Let be i.i.d. -valued random variables. We prove partial moderate deviation principles for self-norm...
National audienceWe prove a self-normalized large deviation principle for sums of Banach space value...
We propose several exponential inequalities for self-normalized martingales similar to those establi...
International audienceWe propose new concentration inequalities for self-normalized martingales. The...
Cram\'er's moderate deviations give a quantitative estimate for the relative error of the normal app...
AbstractLet {Xn,n⩾1} be i.i.d. Rd-valued random variables. We prove partial moderate deviation princ...
We prove a self normalized central limit theorem for a new mixing class of processes introduced in K...
We use the martingale approach to study large deviations and laws of the iterated logarithm for cert...
Cramér type moderate deviation theorems quantify the accuracy of the relative error of the normal ap...
Stein's method is a powerful tool in estimating accuracy of various probability approximations. It w...
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