Selected Publications and  preprints

Goldenshluger, A. and Jacobovic, R. (2024). Smoluchowski processes and nonparametric estimation of functionals of particle displacement distributions from count data.  Ann. Appl. Probab. 34,  1224-1270. 

Goldenshluger, A. and Lepski, O. (2022). Minimax estimation of norms of a probability density: II. Rate-optimal estimation procedures.  Bernoulli 28, 1155-1178.

Goldenshluger, A. and Lepski, O. (2022). Minimax estimation of norms of a probability density: I. Lower bounds.   Bernoulli 28, 1120-1154.

Goldenshluger, A. and Zeevi, A. (2022).  Optimal stopping of a random sequence with unknown distribution.  Mathematics of  Operations Research 47, 29-49.

Goldenshluger, A. and Kim, T. (2021). Density deconvolution with non–standard error distributions: Rates of convergence and adaptive estimation. Electron. J. Statist. 15, 3394-3427.

Belomestny, D. and Goldenshluger, A. (2021). Density deconvolution under general assumptions on the distribution of measurement errors.  Ann. Statist.  49, 615-649.

Goldenshluger, A., Malinovsky, Y. and Zeevi, A. (2020). A unified approach for solving sequential selection problems. Probability Surveys 17, 214-256.

Belomestny, D. and Goldenshluger, A. (2020).  Nonparametric density estimation from observations with multiplicative measurement errors.   Ann. Inst. Henri Poincaré Probab. Stat.    56, 36-67.

 Goldenshluger, A. and Koops, D. T. (2019). Nonparametric estimation of service time characteristics in infinite-server queues with nonstationary Poisson input. Stochastic Systems   9,  183-207. 

Goldenshluger, A. (2018). The M/G/∞ estimation problem revisited. Bernoulli   24, 2531-2568.

Goldenshluger, A. and Mirkin, L. (2017). On minimum-variance event-triggered control. IEEE Control Systems Letters    1, 32-37.

Goldenshluger, A. (2016). Nonparametric estimation of the service time distribution in the M/G/∞ queue. Adv. Appl. Probab.   48, 1117-1138.

Goldenshluger, A., Juditsky, A. and Nemirovski, A. (2015). Hypotheses testing by convex optimization. With discussion and rejoinder. Electron. J. Statist.  9, 1645-1712.

Goldenshluger, A. and Lepski, O. (2014). On adaptive minimax density estimation on Rd.  Probab. Theory Related Fields  159, 479-543.

Goldenshluger A. and Lepski, O. (2013). General selection rule from a family of linear estimators. Theory Probab. Appl.  57, 209-226.

Goldenshluger, A. and Zeevi, A. (2013). A linear response bandit problem. Stochastic Systems   3, 231-262.

Dattner, I., Goldenshluger, A. and Juditsky, A. (2011). On deconvolution of distribution functions. Ann. Statist.  39, 2477-2501.

Goldenshluger, A. and Lepski, O. (2011). Bandwidth selection in kernel density estimation: oracle inequalities and adaptive minimax optimality. Ann. Statist.  39, 1608-1632.

Goldenshluger, A. and Lepski, O. (2011). Uniform bounds for norms of sums of independent random functions. Ann. Probab.  39, 2318-2384.

Goldenshluger, A. and Zeevi, A. (2011). A note on performance limitation in bandit problems with side observations. IEEE Trans. Information Theory  57, 1707--1713.

Goldenshluger, A. and Zeevi, A. (2009). Woodroofe's one-armed bandit problem revisited. Ann. Appl. Probab.  19, 1603-1633.

Goldenshluger, A. (2009). A universal procedure for aggregating estimators. Ann. Statist.  37, 542-568.

Goldenshluger, A. and Lepski, O. (2009).  Structural adaptation via Lp-norm oracle inequalities.  Probab. Theory and Related Fields   143, 41-71.

Goldenshluger, A. and Lepski, O. (2008). Universal pointwise selection rule in multivariate function estimation. Bernoulli  14, 1150-1190.

Goldenshluger, A., Juditsky, A., Tsybakov, A. and Zeevi, A. (2008). Change-point estimation from indirect observations. 2. Adaptation. Ann. Inst. H. Poincare Probab. Stat.  44, 819-836.

Goldenshluger, A., Juditsky, A., Tsybakov, A. and Zeevi, A. (2008). Change-point estimation from indirect observations. 1. Minimax complexity. Ann. Inst. H. Poincare Probab. Stat.  44, 787-818.

Goldenshluger, A., Tsybakov, A. and Zeevi, A. (2006). Optimal change-point estimation from indirect observations. Ann. Statist.  34, 350-372.

Goldenshluger, A. and Spokoiny, V. (2006). Recovering convex edges of an image from noisy tomographic data. IEEE Trans. Information Theory  52, 1322-1334.

Goldenshluger, A. and Spokoiny, V. (2004). On the shape-from-moments problem and recovering edges from noisy Radon data. Probab. Theory and Related Fields  128, 123-140.

Goldenshluger, A. and Zeevi, A. (2004). The Hough transform estimator. Ann. Statist.  32, 1908-1932

Goldenshluger, A. and Pereverzev, S. (2003). On adaptive inverse estimation of linear functionals in Hilbert scales. Bernoulli  9, 783-807

Goldenshluger, A. and Tsybakov, A. (2003). Optimal prediction for linear regression with infinitely many parameters. J. Multivariate Anal.  84, 40-60.

Goldenshluger, A. (2002). Density deconvolution in the circular structural model. J. Multivariate Anal.  81, 350-375.

Goldenshluger, A. and Tsybakov, A. (2001). Adaptive prediction and estimation in linear regression with infinitely many parameters. Ann. Statist.  29, 1601-1619.

Goldenshluger, A. and Pereverzev, S. (2000). Adaptive estimation of linear functionals in Hilbert scales from indirect white noise observations. Probab. Theory and Related Fields  118, 169-186.

Goldenshluger, A. and Faraggi, D. (2000). Nonparameteric estimation of weighted proportions with application to particle size measurement. Biometrika  87, 157-171.

Goldenshluger, A. (1999). On pointwise adaptive nonparametric deconvolution. Bernoulli   5, 907-925.

Goldenshluger, A. (1998). Nonparametric estimation of transfer functions: rates of convergence and adaptation. IEEE Trans. Information Theory  44, 644-658.

Goldenshluger, A. and Nemirovski, A. (1997). Adaptive de-noising of signals satisfying differential inequalities. IEEE Trans. Information Theory  43, 872-889.

Goldenshluger, A. and Nemirovski, A. (1997). On spatially adaptive estimation of nonparametric regression. Math. Methods Statist.  6, 135-170.

Goldenshluger, A. and Polyak, B. (1993). Estimation of regression parameters with arbitrary noise. Math. Methods Statist.  2, 18-29.