V. Spokoiny

发表

On the Efficiency of Wavelet Estimators under Arbitrary Error Distributions

Michael H. Neumann, V. Spokoiny, 1994 .

Structure adaptive approach for dimension reduction

J. Polzehl, V. Spokoiny, M. Hristache, 2001 .

Direct estimation of the index coefficient in a single-index model

A. Juditsky, V. Spokoiny, M. Hristache, 2001 .

Statistical Experiments and Decisions: Asymptotic Theory

A. Shiryaev, V. Spokoiny, 2000 .

Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors

E. Mammen, V. Spokoiny, O. Lepski, 1997 .

Optimal pointwise adaptive methods in nonparametric estimation

V. Spokoiny, O. Lepski, 1997 .

Construction of Non-asymptotic Confidence Sets in 2-Wasserstein Space

V. Spokoiny, Johannes Ebert, A. Suvorikova, 2017, 1703.03658.

Spatial aggregation of local likelihood estimates with applications to classification

V. Spokoiny, D. Belomestny, 2007, 0712.0939.

Inhomogeneous Dependence Modeling with Time-Varying Copulae

W. Härdle, V. Spokoiny, E. Giacomini, 2009 .

Image denoising: Pointwise adaptive approach

J. Polzehl, V. Spokoiny, 2003 .

Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations

P. Dvurechensky, A. Gasnikov, V. Spokoiny, 2015, 1508.00858.

Propagation-Separation Approach for Local Likelihood Estimation

J. Polzehl, V. Spokoiny, 2006 .

On estimation of the Lr norm of a regression function

A. Nemirovski, V. Spokoiny, O. Lepski, 1999 .

Minimax Nonparametric Hypothesis Testing: The Case of an Inhomogeneous Alternative

V. Spokoiny, O. Lepski, 1999 .

Adaptive weights smoothing with applications to image restoration

J. Polzehl, V. Spokoiny, J. Polzehl, 1998 .

Random Gradient-Free Minimization of Convex Functions

Yurii Nesterov, Vladimir G. Spokoiny, Y. Nesterov, 2015, Foundations of Computational Mathematics.

Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

D. Ginsbourger, V. Spokoiny, Jean-Michel Loubes, 2018, Electronic Journal of Statistics.

Gaussian Process Forecast with multidimensional distributional entries

V. Spokoiny, Jean-Michel Loubes, A. Suvorikova, 2018, 1805.00753.

Structure Adaptive Approach for Dimension Reduction 1

A. Juditsky, J. Polzehl, V. Spokoiny, 2001 .

Component Identification and Estimation in Nonlinear High-Dimensional Regression Models by Structural Adaptation

V. Spokoiny, A. Samarov, C. Vial, 2005 .

Bernstein - von Mises Theorem for growing parameter dimension

V. Spokoiny, 2013, 1302.3430.

Critical dimension in the semiparametric Bernstein—von Mises theorem

V. Spokoiny, Maxim Panov, 2014 .

Finite Sample Bernstein -- von Mises Theorem for Semiparametric Problems

V. Spokoiny, Maxim Panov, 2013, 1310.7796.

Parametric estimation. Finite sample theory

V. Spokoiny, 2011, 1111.3029.

Sharp deviation bounds for quadratic forms

V. Spokoiny, M. Zhilova, 2013, 1302.1699.

Multiscale testing of qualitative hypotheses

V. Spokoiny, L. Dümbgen, 2001 .

Adaptive Gradient Descent for Convex and Non-Convex Stochastic Optimization

Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky, 2019, 1911.08380.

Two convergence results for an alternation maximization procedure

V. Spokoiny, A. Andresen, 2015, 1501.01525.

GHICA - Risk Analysis with GH Distributions and Independent Components

W. Härdle, V. Spokoiny, Ying Chen, 2006 .

On the efficiency of a randomized mirror descent algorithm in online optimization problems

Y. Nesterov, A. Gasnikov, V. Spokoiny, 2015, Computational Mathematics and Mathematical Physics.

Adaptive hypothesis testing using wavelets

V. Spokoiny, 1996 .

Local likelihood modeling by adaptive weights smoothing

J. Polzehl, V. Spokoiny, 2004 .

Structural Adaptive Smoothing Procedures

J. Polzehl, R. Dahlhaus, P. Mrázek, 2008 .

Optimal stopping via deeply boosted backward regression

V. Spokoiny, D. Belomestny, J. Schoenmakers, 2018 .

Portfolio value at risk based on independent component analysis

W. Härdle, V. Spokoiny, Ying Chen, 2007 .

Statistical inference for time-inhomogeneous volatility models

V. Spokoiny, Danilo Mercurio, 2004, math/0406430.

Multiscale local change point detection with applications to value-at-risk

V. Spokoiny, 2009, 0906.1698.

Sparse NonGaussian Component Analysis

A. Juditsky, V. Spokoiny, Elmar Diederichs, 2009, 0904.0430.

Time Inhomogeneous Multiple Volatility Modeling

Helmut Herwartz, V. Spokoiny, W. Haerdle, 2003 .

On large-deviation efficiency in statistical inference

Anatolii A. Puhalskii, Vladimir Spokoiny, V. Spokoiny, 1998 .

Estimation in Linear Models

T. Dickhaus, V. Spokoiny, 2015 .

Statistical inference for Bures–Wasserstein barycenters

Alexey Kroshnin, Vladimir Spokoiny, Alexandra Suvorikova, 2019, The Annals of Applied Probability.

Structure-adaptive Manifold Estimation

V. Spokoiny, Nikita Puchkin, 2019, J. Mach. Learn. Res..

Optimal stopping via reinforced regression

Denis Belomestny, John Schoenmakers, Vladimir Spokoiny, 2018, Communications in Mathematical Sciences.

N A ] 7 A ug 2 01 8 Optimal stopping via deeply boosted backward regression

V. Spokoiny, D. Belomestny, J. Schoenmakers, 2018 .

Varying coefficient regression modeling by adaptive weights smoothing

Jörg Polzehl, Vladimir Spokoiny, J. Polzehl, 2003 .

Mathematisches Forschungsinstitut Oberwolfach Modern Nonparametric Statistics: Going beyond Asymptotic Minimax

Iain M. Johnstone, Lucien Birgé, Vladimir Spokoiny, 2010 .

Bootstrap tuning in Gaussian ordered model selection

V. Spokoiny, N. Willrich, 2019, The Annals of Statistics.

Bootstrap tuning in model choice problem

V. Spokoiny, Kirill Efimov, 2015 .

Bootstrap tuning in ordered model selection

Vladimir Spokoiny, V. Spokoiny, N. Willrich, 2015 .

Spatially adaptive density estimation by localised Haar projections

Richard Nickl, Vladimir Spokoiny, Florian Gach, 2011, 1111.2807.

Multivariate Brenier cumulative distribution functions and their application to non-parametric testing

V. Spokoiny, A. Suvorikova, Melf Boeckel, 2018, 1809.04090.

Manifold-based time series forecasting

V. Spokoiny, Nikita Puchkin, A. Timofeev, 2020, 2012.08244.

Martingale approach in pricing and hedging European options under regime-switching

Vladimir Spokoiny, Grigori N. Milstein, V. Spokoiny, 2011 .

Forward and reverse representations for Markov chains ∗

V. Spokoiny, G. Milstein, J. Schoenmakers, 2006 .

Discussion Paper 2006-041 Forward and reverse representations for Markov chains

V. Spokoiny, G. Milstein, J. Schoenmakers, 2006 .

Multiscale parametric approach for change point detection

V. Spokoiny, A. Suvorikova, N. Buzun, 2015 .

SPATIALLY ADAPTIVE NON-GAUSSIAN IMAGING VIA FITTED LOCAL LIKELIHOOD TECHNIQUE

V. Katkovnik, V. Spokoiny, 2006 .

Re-sampling in instrumental variables regression

M. Jirak, V. Spokoiny, A. Naumov, 2020 .

Bootstrap condence sets under model misspecication

V. Spokoiny, M. Zhilova, 2014 .

Efficient estimation and experimental design for controlled systems. Asymptotic approach

V. Spokoiny, 1994 .

Semiparametric Single Index Versus Fixed Link Function Modelling

W. Härdle, V. Spokoiny, S. Sperlich, 1997 .

Adaptive Manifold Clustering

V. Spokoiny, Franz Besold, 2019, 1912.04869.

Large ball probabilities, Gaussian comparison and anti-concentration

V. Spokoiny, A. Naumov, F. Gotze, 2017, Bernoulli.

Accuracy of Gaussian approximation in nonparametric Bernstein -- von Mises Theorem

V. Spokoiny, M. Panov, 2019, 1910.06028.

Gaussian comparison and anti-concentration inequalities for norms of Gaussian random elements

F. Götze, V. Spokoiny, A. Naumov, 2017 .

On Estimating a Dynamic Function of a Stochastic System with Averaging

V. Spokoiny, R. Liptser, 2000 .

STRUCTURE ADAPTIVE METHODS IN IMAGE DENOISING

V. Spokoiny, 2008 .

Structural Adaptive Smoothing by Propagation–Separation Methods

J. Polzehl, V. Spokoiny, 2008 .

Estimation in Linear Models

T. Dickhaus, V. Spokoiny, 2015 .

On large-deviation ef®ciency in statistical inference

V. Spokoiny, 2007 .

Bayesian inference for nonlinear inverse problems

V. Spokoiny, 2019, 1912.12694.

ST ] 1 5 O ct 2 01 9 Accuracy of Gaussian approximation in nonparametric

V. Spokoiny, 2019 .

Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation

V. Spokoiny, 2022, ArXiv.

Instrumental Variables Regression

V. Spokoiny, Andzhey Koziuk, 2018, Foundations of Modern Econometrics.

Confidence Sets for Spectral Projectors of Covariance Matrices

V. Spokoiny, A. Naumov, V. Ulyanov, 2018, Doklady Mathematics.

Bayesian inference for spectral projectors of the covariance matrix

V. Spokoiny, I. Silin, 2017, 1711.11532.

Locally Time Homogeneous Time Series Modelling

V. Spokoiny, M. Elagin, 2009 .

Multiplier bootstrap for Bures-Wasserstein barycenters

Alexey Kroshnin, Vladimir Spokoiny, Alexandra Suvorikova, 2021, 2111.12612.

Efficient randomized mirror descents in stochastic online convex optimization

Y. Nesterov, A. Gasnikov, V. Spokoiny, 2014, 1410.3118.

Adaptive Weights Community Detection

V. Spokoiny, Franz Besold, 2022, 2208.11994.

Statistical inference for time-inhomogeneous volatility models

V. Spokoiny, Danilo Mercurio, 2004, math/0406430.

A New Algorithm for Estimating the Effective Dimension-Reduction Subspace

A. Juditsky, A. Dalalyan, V. Spokoiny, 2007, J. Mach. Learn. Res..

Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

David Ginsbourger, Jean-Michel Loubes, François Bachoc, 2018, Electronic Journal of Statistics.

Bootstrap confidence sets for spectral projectors of sample covariance

V. Spokoiny, A. Naumov, V. Ulyanov, 2018, Probability Theory and Related Fields.

Random Gradient-Free Minimization of Convex Functions

Y. Nesterov, V. Spokoiny, 2015, Foundations of Computational Mathematics.

On the efficiency of a randomized mirror descent algorithm in online optimization problems

Y. Nesterov, A. Gasnikov, V. Spokoiny, 2015, Computational Mathematics and Mathematical Physics.

Sharp deviation bounds for quadratic forms

V. Spokoiny, M. Zhilova, 2013, Mathematical Methods of Statistics.

Regression methods in pricing American and Bermudan options using consumption processes

V. Spokoiny, G. Milstein, D. Belomestny, 2009 .

Bayesian Model Selection and the Concentration of the Posterior of Hyperparameters

V. Spokoiny, N. Baldin, 2014, Journal of Mathematical Sciences.

Accelerated gradient methods with absolute and relative noise in the gradient

P. Dvurechensky, A. Gasnikov, V. Spokoiny, 2021, Optimization Methods and Software.

Accuracy of Gaussian approximation for high-dimensional posterior distributions

V. Spokoiny, Maxim Panov, 2021 .

Basics of Modern Mathematical Statistics

Vladimir Spokoiny, Thorsten Dickhaus, T. Dickhaus, 2014 .

Structural Tests in Additive Regression

Wolfgang Härdle, Stefan Sperlich, Vladimir Spokoiny, 2001 .

An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative

Joel L. Horowitz, Vladimir Spokoiny, J. Horowitz, 2001 .

Data-driven testing the fit of linear models

V. Spokoiny, 2000 .

Reinforced optimal control

V. Spokoiny, D. Belomestny, J. Schoenmakers, 2020, Communications in Mathematical Sciences.

In Search of Non-Gaussian Components of a High-Dimensional Distribution

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, J. Mach. Learn. Res..

A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, ICA.

Adaptive Nonparametric Clustering

Vladimir Spokoiny, Kirill Efimov, Larisa Adamyan, 2017, IEEE Transactions on Information Theory.

Diffusion tensor imaging: Structural adaptive smoothing

Karsten Tabelow, Jörg Polzehl, Henning U. Voss, 2008, NeuroImage.

Component analysis for additive models

W. Härdle, V. Spokoiny, S. Sperlich, 1997 .

Sparse non Gaussian component analysis by semidefinite programming

Arkadi Nemirovski, Anatoli B. Juditsky, Vladimir G. Spokoiny, 2013, Machine Learning.

Sparse Non-Gaussian Component Analysis

Christof Schütte, Anatoli B. Juditsky, Vladimir G. Spokoiny, 2010, IEEE Transactions on Information Theory.

Uniform properties of the local maximum likelihood estimate

Mayya M. Zhilova, Vladimir G. Spokoiny, V. Spokoiny, 2013, Autom. Remote. Control..

Spatially Adaptive Estimation via Fitted Local Likelihood Techniques

Vladimir Katkovnik, Vladimir G. Spokoiny, V. Katkovnik, 2008, IEEE Transactions on Signal Processing.

Multiscale Testing of Qualitative Hypotheses 1

V. Spokoiny, L. Dümbgen, 2001 .

Stopping rules for accelerated gradient methods with additive noise in gradient

Alexander Gasnikov, Vladimir Spokoiny, Vasin Artem, 2021 .

ST ] 2 A pr 2 00 9 Sparse NonGaussian Component Analysis ∗

C. Schütte, V. Spokoiny, Elmar Diederichs, 2009 .

Bootstrap confidence sets under model misspecification

Vladimir Spokoiny, Mayya Zhilova, V. Spokoiny, 2014, 1410.0347.

Penalized maximum likelihood estimation and effective dimension

Vladimir Spokoiny, V. Spokoiny, 2012, 1205.0498.

Statistics of extremes by oracle estimation

V. Spokoiny, I. Grama, Ion Grama, 2008, 0808.0976.

ADAPTIVE ESTIMATION OF THE EXCESS OF A DISTRIBUTION FUNCTION

V. Spokoiny, I. Grama, 2006 .

Some Other Testing Methods

T. Dickhaus, V. Spokoiny, 2015 .

Earthquake prediction using the fields estimated by an adaptive algorithm

Alexander Derendyaev, Valeri Gitis, Sergey A. Pirogov, 2017, WIMS.

Construction of Mean-Self-Financing Strategies for European Options under Regime-Switching

G. N. Milstein, Vladimir Spokoiny, V. Spokoiny, 2014, SIAM J. Financial Math..

Testing a Linear Hypothesis Using Haar Transform Testing a Linear Hypothesis Using Haar Transform 1

V. Spokoiny, 1997 .

Testing in Linear Models

T. Dickhaus, V. Spokoiny, 2015 .

Local Quantile Regression

Weining Wang, Wolfgang K. Härdle, W. Härdle, 2010, 1208.5384.

Parameter tuning in pointwise adaptation using a propagation approach

Vladimir Spokoiny, V. Spokoiny, C. Vial, 2009, 0908.3596.

Spatially adaptive regression estimation: Propagation-separation approach

V. Spokoiny, J. Polzehl, 2005 .

Analyzing fMRI experiments with structural adaptive smoothing procedures

Karsten Tabelow, Jörg Polzehl, Henning U. Voss, 2006, NeuroImage.

A penalized exponential risk bound in parametric estimation

V. Spokoiny, V. Spokoiny, 2009, 0903.1721.

Exponential bounds for minimum contrast estimators

V. Spokoiny, Yu. Golubev, 2009, 0901.0655.

Varying coefficient GARCH models

V. Spokoiny, P. Čížek, 2009 .

Efficient and positive semidefinite pre-averaging realized covariance estimator

Vladimir Spokoiny, Liang-Ching Lin, Guangming Pan, 2021 .

Estimation of time dependent volatility via local change point analysis

V. Spokoiny, Danilo Mercurio, 2004 .

On the shape–from–moments problem and recovering edges from noisy Radon data

Vladimir Spokoiny, Alexander Goldenshluger, A. Goldenshluger, 2004 .

Pareto approximation of the tail by local exponential modeling.

V. Spokoiny, I. Grama, 2008 .

Critical dimension in profile semiparametric estimation

V. Spokoiny, A. Andresen, 2013, 1303.4640.

Adaptive Estimation for a Time Inhomogeneous Stochastic-Volatility Model

Wolfgang Härdle, Vladimir Spokoiny, Gilles Teyssière, 2000 .

Deviation probability bound for martingales with applications to statistical estimation

Vladimir Spokoiny, Robert Liptser, V. Spokoiny, 2000 .

On sequential estimation of an autoregressive parameter

A. Shiryaev, V. Spokoiny, 1997 .

Functional and dynamic magnetic resonance imaging using vector adaptive weights smoothing

Jörg Polzehl, Vladimir Spokoiny, J. Polzehl, 2001 .

ESTIMATION OF A FUNCTION WITH DISCONTINUITIES VIA LOCAL POLYNOMIAL FIT WITH AN ADAPTIVE WINDOW CHOICE

Vladimir Spokoiny, V. Spokoiny, 1998 .

Testing a Statistical Hypothesis

Thorsten Dickhaus, Vladimir Spokoiny, T. Dickhaus, 2015 .

Mixed Laplace approximation for marginal posterior and Bayesian inference in error-in-operator model

V. Spokoiny, 2023, 2305.09336.

Finite samples inference and critical dimension for stochastically linear models

V. Spokoiny, 2022, 2201.06327.

Bootstrap confidence sets for spectral projectors of sample covariance

Vladimir Spokoiny, Alexey Naumov, Vladimir Ulyanov, 2017, Probability Theory and Related Fields.

Sparse non Gaussian component analysis by semidefinite programming

A. Juditsky, A. Nemirovski, V. Spokoiny, 2011, Machine Learning.

Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2005, NIPS.

Properties of the Bayesian Parameter Estimation of a Regression Based on Gaussian Processes

Evgeny Burnaev, Vladimir Spokoiny, Alexey Zaytsev, 2014 .

Properties of the Bayesian Parameter Estimation of a Regression Based on Gaussian Processes

V. Spokoiny, E. Burnaev, A. A. Zaytsev, 2014, Journal of Mathematical Sciences.

Properties of the posterior distribution of a regression model based on Gaussian random fields

Evgeny V. Burnaev, Vladimir G. Spokoiny, Alexey A. Zaitsev, 2013, Autom. Remote. Control..

Properties of the posterior distribution of a regression model based on Gaussian random fields

V. Spokoiny, E. Burnaev, A. Zaitsev, 2013, Automation and Remote Control.

Reconstruction of manifold embeddings into Euclidean spaces via intrinsic distances

V. Spokoiny, E. Stepanov, Nikita Puchkin, 2020, ESAIM: Control, Optimisation and Calculus of Variations.

Confidence estimation of the covariance function of stationary and locally stationary processes

V. Spokoiny, M. Giurcanu, 2004 .

Forward and reverse representations for Markov chains

V. Spokoiny, G. Milstein, J. Schoenmakers, 2007 .

Transition density estimation for stochastic differential equations via forward-reverse representations

John Schoenmakers, Vladimir Spokoiny, Grigori N. Milstein, 2004 .

Pointwise adaptation via stagewise aggregation of local estimates for multiclass classification pdf

Vladimir G. Spokoiny, Nikita Puchkin, V. Spokoiny, 2018, ArXiv.

Robust Risk Management. Accounting for Nonstationarity and Heavy Tails

V. Spokoiny, Ying Chen, 2007 .

Time Inhomogeneous Multiple Volatility Modeling

W. Härdle, V. Spokoiny, H. Herwartz, 2003 .

Dynamic nonparametric filtering with application to volatility estimation

Ming-Yen Cheng, Vladimir Spokoiny, Jianqing Fan, 2003 .

Adaptive Pointwise Estimation in Time-Inhomogeneous Conditional Heteroscedasticity Models

Wolfgang K. Härdle, W. Härdle, V. Spokoiny, 2009, 0903.4620.

Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models

Wolfgang K. Härdle, W. Härdle, V. Spokoiny, 2007 .

Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

D. Ginsbourger, V. Spokoiny, Jean-Michel Loubes, 2018, Electronic Journal of Statistics.

Adaptive drift estimation for nonparametric diffusion model

Vladimir Spokoiny, V. Spokoiny, 2000 .

Convergence of an Alternating Maximization Procedure

Vladimir G. Spokoiny, Andreas Andresen, V. Spokoiny, 2016, J. Mach. Learn. Res..

Inhomogeneous Dependency Modelling with Time Varying Copulae

W. Härdle, V. Spokoiny, E. Giacomini, 2006 .

Lower bounds of minimax risk in estimation problems without lan condition

V. Spokoiny, 1992 .

An Adaptive, Rate-Optimal Test of a Parametric Model Against a Nonparametric Alternative

Joel L. Horowitz, Vladimir Spokoiny, J. Horowitz, 1999 .

An adaptive multiclass nearest neighbor classifier

V. Spokoiny, Nikita Puchkin, 2018, ESAIM: Probability and Statistics.

Optimal choice of observation window for Poisson observations

V. Spokoiny, Y. Kutoyants, 1999 .

Large ball probabilities, Gaussian comparison and anti-concentration

V. Spokoiny, A. Naumov, F. Gotze, 2017, Bernoulli.

Spatially adaptive density estimation by localised Haar projections

V. Spokoiny, F. Gach, Richard Nickl, 2011, 1111.2807.

Multiscale local change point detection with applications to value-at-risk

V. Spokoiny, 2009, 0906.1698.

High dimensional change-point detection: a complete graph approach

V. Spokoiny, Katerina Papagiannouli, Yangjie Sun, 2022, ArXiv.

Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation

V. Spokoiny, 2022, SIAM/ASA J. Uncertain. Quantification.

MODERATE DEVIATIONS TYPE EVALUATION FOR INTEGRAL FUNCTIONALS OF DIFFUSION PROCESSES

V. Spokoiny, R. Liptser, 1999 .

Freidlin-Wentzell type large deviations for smooth processes

Vladimir Spokoiny, Robert Liptser, A. Veretennikov, 2002 .