Timothy Armstrong, Martin Weidner, Andrei Zeleneev, “Robust Estimation and Inference in Panels with Interactive Fixed Effects”, Journal of Political Economy, 2026
Avijit Paul, Srivalleesha Mallidi, “Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution”, AIP Advances, 15:5 (2025)
Subhodh Kotekal, Chao Gao, “Sparsity meets correlation in Gaussian sequence model”, Ann. Statist., 53:3 (2025)
Б. Я. Левит, “Минимаксное линейное оценивание на полупрямой с использованием преобразования Меллина”, Теория вероятн. и ее примен., 70:2 (2025), 228–246; B. Y. Levit, “Minimax linear estimation on the half-line via Mellin transform”, Theory Probab. Appl., 70:2 (2025), 185–199
В. Н. Солев, “Сопряженная система и точность в задаче оценивания”, Вероятность и статистика. 37, Зап. научн. сем. ПОМИ, 544, ПОМИ, СПб., 2025, 314–328
В. Н. Солев, “Аппроксимация спектральной плотности и точность в задаче оценивания”, Вероятность и статистика. 36, Зап. научн. сем. ПОМИ, 535, ПОМИ, СПб., 2024, 255–268
В. Н. Солев, “Пространство BMO и задача оценивания функции, наблюдаемой на фоне гауссовского стационарного шума”, Вероятность и статистика. 35, Посвящается юбилею Яны Исаевны БЕЛОПОЛЬСКОЙ, Зап. научн. сем. ПОМИ, 526, ПОМИ, СПб., 2023, 193–206
Sergio Brenner Miguel, Fabienne Comte, Jan Johannes, “Linear functional estimation under multiplicative measurement error”, Bernoulli, 29:3 (2023)
Matey Neykov, “On the Minimax Rate of the Gaussian Sequence Model Under Bounded Convex Constraints”, IEEE Trans. Inform. Theory, 69:2 (2023), 1244
David A. Hirshberg, Stefan Wager, “Augmented minimax linear estimation”, Ann. Statist., 49:6 (2021)
Alexandra Carpentier, Sylvain Delattre, Etienne Roquain, Nicolas Verzelen, “Estimating minimum effect with outlier selection”, Ann. Statist., 49:1 (2021)
Clément de Chaisemartin, “The Minimax Estimator of the Average Treatment Effect, among Linear Combinations of Conditional Average Treatment Effects Estimators”, SSRN Journal, 2021
Г. К. Голубев, “Об адаптивном оценивании линейных функционалов по наблюдениям в белом шуме”, Пробл. передачи информ., 56:2 (2020), 95–111; G. K. Golubev, “On adaptive estimation of linear functionals from observations against white noise”, Problems Inform. Transmission, 56:2 (2020), 185–200
Anatoli Juditsky, Arkadi Nemirovski, “Near-optimal recovery of linear and N-convex functions on unions of convex sets”, Information and Inference: A Journal of the IMA, 9:2 (2020), 423
Arlene K. H. Kim, “Obtaining minimax lower bounds: a review”, J. Korean Stat. Soc., 49:3 (2020), 673
Debashis Ghosh, “Statistical Inference via Convex Optimization Nillas, Alice Anatoli Juditsky and Arkadi Nemirovski Princeton University Press, 2020, xiv + 656 pages, £ 70/$85, hardcover ISBN: 978‐0‐6911‐9729‐6”, Int Statistical Rev, 88:3 (2020), 806
Timothy B. Armstrong, Michal Kolesár, “Simple and honest confidence intervals in nonparametric regression”, QE, 11:1 (2020), 1
Guido Imbens, Stefan Wager, “Optimized Regression Discontinuity Designs”, The Review of Economics and Statistics, 101:2 (2019), 264
В. Н. Солев, “Oценка функции в гауссовском стационарном шуме: новые спектральные условия”, Вероятность и статистика. 27, Зап. научн. сем. ПОМИ, 474, ПОМИ, СПб., 2018, 222–232
Timothy Armstrong, Michal Kolessr, “Simple and Honest Confidence Intervals in Nonparametric Regression”, SSRN Journal, 2018
Wayne Yuan Gao, “Minimax linear estimation at a boundary point”, Journal of Multivariate Analysis, 165 (2018), 262
Timothy Armstrong, Michal Kolesár, “Simple and Honest Confidence Intervals in Nonparametric Regression”, SSRN Journal, 2018
В. Н. Солев, “Локальная версия условия Маккенхаупта и точность оценивания неизвестной псевдо-периодической функции, наблюдаемой на фоне стационарного шума”, Вероятность и статистика. 26, Зап. научн. сем. ПОМИ, 466, ПОМИ, СПб., 2017, 289–299
Timothy B. Armstrong, Michal Kolessr, “Optimal Inference in a Class of Regression Models”, SSRN Journal, 2017
Wayne Gao, “Minimax Linear Estimation at a Boundary Point”, SSRN Journal, 2017
Timothy B. Armstrong, Michal Kolessr, “Optimal Inference in a Class of Regression Models”, SSRN Journal, 2017
В. Н. Солев, “Адаптивная оценка функции, наблюдаемой на фоне гауссовского стационарного шума”, Вероятность и статистика. 24, Зап. научн. сем. ПОМИ, 454, ПОМИ, СПб., 2016, 261–275; V. N. Solev, “Adaptive estimation of function observed in Gaussian stationary noise”, J. Math. Sci. (N. Y.), 229:6 (2018), 772–781
Christoph Breunig, Jan Johannes, “ADAPTIVE ESTIMATION OF FUNCTIONALS IN NONPARAMETRIC INSTRUMENTAL REGRESSION”, Econom. Theory, 32:3 (2016), 612
Timothy B. Armstrong, Michal Kolessr, “Simple and Honest Confidence Intervals in Nonparametric Regression”, SSRN Journal, 2016
Timothy B. Armstrong, Michal Kolesar, “Simple and Honest Confidence Intervals in Nonparametric Regression”, SSRN Journal, 2016
В. Н. Солев, “Оценка функции, наблюдаемой на фоне стационарного шума: дискретизация”, Вероятность и статистика. 22, Зап. научн. сем. ПОМИ, 441, ПОМИ, СПб., 2015, 286–298; V. N. Solev, “Estimation of function observed in stationary noise: discretization”, J. Math. Sci. (N. Y.), 219:5 (2016), 798–806
Alexander Goldenshluger, Anatoli Juditsky, Arkadi Nemirovski, “Hypothesis testing by convex optimization”, Electron. J. Statist., 9:2 (2015)
В. Н. Солев, “Условие Маккенхаупта и одна задача оценивания”, Вероятность и статистика. 21, Посвящается юбилею Михаила Иосифовича ГОРДИНА, Зап. научн. сем. ПОМИ, 431, ПОМИ, СПб., 2014, 186–197; V. N. Solev, “Mackenhoupt condition and an estimating problem”, J. Math. Sci. (N. Y.), 214:4 (2016), 546–553
Y. Ritov, P. J. Bickel, A. C. Gamst, B. J. K. Kleijn, “The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA?”, Statist. Sci., 29:4 (2014)
N. Stepanova, “On estimation of analytic density functions in L p”, Math. Meth. Stat., 22:2 (2013), 114
Jan Johannes, Rudolf Schenk, “On rate optimal local estimation in functional linear regression”, Electron. J. Statist., 7:none (2013)
Ingster Yu.I. Sapatinas T. Suslina I.A., “Minimax Signal Detection in Ill-Posed Inverse Problems”, Ann. Stat., 40:3 (2012), 1524–1549
J. Johannes, R. Schenk, “Adaptive estimation of linear functionals in functional linear models”, Math. Meth. Stat., 21:3 (2012), 189
XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan, “Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization”, IEEE Trans. Inform. Theory, 56:11 (2010), 5847
С. В. Решетов, “Минимаксный риск для квадратично выпуклых множеств”, Вероятность и статистика. 15, Зап. научн. сем. ПОМИ, 368, ПОМИ, СПб., 2009, 181–189; S. V. Reshetov, “Minimax risk for quadratically convex sets”, J. Math. Sci. (N. Y.), 167:4 (2010), 537–542
Meng Zhao, K.B. Kulasekera, “Minimax estimation of linear functionals under squared error loss”, Journal of Statistical Planning and Inference, 139:9 (2009), 3160
Peter T. Kim, Ja-Yong Koo, Zhi-Ming Luo, “Weyl eigenvalue asymptotics and sharp adaptation on vector bundles”, Journal of Multivariate Analysis, 100:9 (2009), 1962
Alexander Meister, “Uniform and individual convergence rates for convex density classes”, Statistics & Decisions, 26:1 (2008), 25
Ja-Yong Koo, Peter T. Kim, “Sharp adaptation for spherical inverse problems with applications to medical imaging”, Journal of Multivariate Analysis, 99:2 (2008), 165
E. Belitser, F. Enikeeva, “Empirical bayesian test of the smoothness”, Math. Meth. Stat., 17:1 (2008), 1
Jussi Klemelä, “Sharp adaptive estimation of quadratic functionals”, Probab. Theory Relat. Fields, 134:4 (2006), 539
Klaassen C.A.J., Lee E.J., Ruymgaart F.H., “Asymptotically efficient estimation of linear functionals in inverse regression models”, Journal of Nonparametric Statistics, 17:7 (2005), 819–831
Steven N. Evans, Ben B. Hansen, Philip B. Stark, “Minimax expected measure confidence sets for restricted location parameters”, Bernoulli, 11:4 (2005)
T. Tony Cai, Mark G. Low, “On adaptive estimation of linear functionals”, Ann. Statist., 33:5 (2005)
T. Tony Cai, Mark G. Low, “Nonparametric estimation over shrinking neighborhoods: Superefficiency and adaptation”, Ann. Statist., 33:1 (2005)
Eric Chicken, T. Tony Cai, “Block thresholding for density estimation: local and global adaptivity”, Journal of Multivariate Analysis, 95:1 (2005), 76
Г. К. Голубев, “Метод огибающих риска в оценивании линейных функционалов”, Пробл. передачи информ., 40:1 (2004), 58–72; G. K. Golubev, “Method of Risk Envelopes in Estimation of Linear Functionals”, Problems Inform. Transmission, 40:1 (2004), 53–65
Jussi Klemelä, Alexandre B. Tsybakov, “Exact constants for pointwise adaptive estimation under the Riesz transform”, Probab. Theory Relat. Fields, 129:3 (2004), 441
T. Tony Cai, Mark G. Low, “Minimax estimation of linear functionals over nonconvex parameter spaces”, Ann. Statist., 32:2 (2004)
T. Tony Cai, Mark G. Low, “An adaptation theory for nonparametric confidence intervals”, Ann. Statist., 32:5 (2004)
Goldenshluger A., Pereverzev S.V., “On adaptive inverse estimation of linear functionals in Hilbert scales”, Bernoulli, 9:5 (2003), 783–807
Jussi Klemelä, “Lower bounds for the asymptotic minimax risk with spherical data”, Journal of Statistical Planning and Inference, 113:1 (2003), 113
T. Tony Cai, Mark G. Low, “A note on nonparametric estimation of linear functionals”, Ann. Statist., 31:4 (2003)
Mark G Low, Yung-Gyung Kang, “Estimating monotone functions”, Statistics & Probability Letters, 56:4 (2002), 361
Emmanuel J. Candès, David L. Donoho, “Recovering edges in ill-posed inverse problems: optimality of curvelet frames”, Ann. Statist., 30:3 (2002)
Eduard Belitser, “Minimax recovery of blurred signal from discrete noisy data”, Journal of Nonparametric Statistics, 13:5 (2001), 647
Holger Drees, “Minimax Risk Bounds in Extreme Value Theory”, Ann. Statist., 29:1 (2001)
Jussi Klemelä, Alexandre B. Tsybakov, “Sharp Adaptive Estimation of Linear Functionals”, Ann. Statist., 29:6 (2001)
K. HELMES, C. SRINIVASAN, “CHARACTERISATION OF LINEAR MINI-MAX ESTIMATORS FOR LOSS FUNCTIONS OF ARBITRARY POWER”, Int. Game Theory Rev., 03:02n03 (2001), 203
Laurent Cavalier, “Efficient estimation of a density in a problem of tomography”, Ann. Statist., 28:2 (2000)
A. C. M. Van Rooij, F. H. Ruymgaart, W. R. Van Zwet, “Asymptotic efficiency of inverse estimators”, Теория вероятн. и ее примен., 44:4 (1999), 826–844; A. C. M. Van Rooij, F. H. Ruymgaart, W. R. Van Zwet, “Asymptotic efficiency of inverse estimators”, Theory Probab. Appl., 44:4 (2000), 722–738
A. B. Tsybakov, “Pointwise and sup-norm sharp adaptive estimation of functions on the Sobolev classes”, Ann. Statist., 26:6 (1998)
В. Н. Соловьев, “Двойственные экстремальные задачи и их применения к задачам минимаксного оценивания”, УМН, 52:4(316) (1997), 49–86; V. N. Solov'ev, “Dual extremal problems and their applications to minimax estimation problems”, Russian Math. Surveys, 52:4 (1997), 685–720
Kurt Helmes, C. Srinivasan, Theory and Decision Library, 18, Game Theoretical Applications to Economics and Operations Research, 1997, 1
Linda H. Zhao, “Minimax linear estimation in a white noise problem”, Ann. Statist., 25:2 (1997)
Lawrence D. Brown, Mark G. Low, “A constrained risk inequality with applications to nonparametric functional estimation”, Ann. Statist., 24:6 (1996)
Lawrence D. Brown, Statistical Decision Theory and Related Topics V, 1994, 1
Sam Efromovich, Mark G. Low, “Adaptive estimates of linear functionals”, Probab. Th. Rel. Fields, 98:2 (1994), 261
Ordoukhani Nasser, A. Thavaneswarn, M. Samanta, “Functional version of the delta method and its application”, Communications in Statistics - Theory and Methods, 20:1 (1991), 373
M.E. Thompson, A. Thavaneswaran, “Optimal nonparametric estimation for some semimartingale stochastic differential equations”, Applied Mathematics and Computation, 39:3 (1990), 123s
M.E. Thompson, A. Thavaneswaran, “Optimal nonparametric estimation for some semimartingale stochastic differential equations”, Applied Mathematics and Computation, 37:3 (1990), 169
И. Ф. Пинелис, “О минимаксном риске”, Теория вероятн. и ее примен., 35:1 (1990), 92–97; I. F. Pinelis, “On minimax risk”, Theory Probab. Appl., 35:1 (1990), 104–109
И. Ф. Пинелис, “О минимаксном оценивании регрессии”, Теория вероятн. и ее примен., 35:3 (1990), 494–505; I. F. Pinelis, “Minimax estimation of a regression”, Theory Probab. Appl., 35:3 (1990), 500–512