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20082014
most citedAdaptive asymptotically efficient estimation in heteroscedastic nonparametric regression via model selection

9 citations · 25 across the 10 of their papers we have counts for

collaborators

11 papers

math.ST2014★ 7 cited

Efficient pointwise estimation based on discrete data in ergodic nonparametric diffusions

L. I. Galtchouk, S. M. Pergamenshchikov

A truncated sequential procedure is constructed for estimating the drift coefficient at a given state point based on discrete data of ergodic diffusion process. A nonasymptotic upp…

math.PR2011

Uniform concentration inequality for ergodic diffusion processes observed at discrete times

Leonid Galtchouk, Serguei Pergamenchtchikov

In this paper a concentration inequality is proved for the deviation in the ergodic theorem in the case of discrete time observations of diffusion processes. The proof is based on…

math.PR2010

On some estimates for bounded submartingales and the shift inequality

Leonid Galtchouk, Isaac Sonin

It is well known that if a submartingale is bounded then the increasing predictable process and the martingale from the Doob decomposition can be unbounded. I…

math.ST2010★ 3 cited

Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models

Leonid Galtchouk, Serguei Pergamenchtchikov

An adaptive nonparametric estimation procedure is constructed for heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound…

math.ST2010

Adaptive asymptotically efficient estimation in heteroscedastic nonparametric regression

Leonid Galtchouk, Serguei Pergamenchtchikov

The paper deals with asymptotic properties of the adaptive procedure proposed in the author paper, 2007, for estimating an unknown nonparametric regression. %\cite{GaPe1}. We prove…

math.PR2010★ 4 cited

Geometric ergodicity for families of homogeneous Markov chains

Leonid Galtchouk, Serguei Pergamenchtchikov

In this paper we find nonasymptotic exponential upper bounds for the deviation in the ergodic theorem for families of homogeneous Markov processes. We find some sufficient conditio…