Yr Athro Anatoly Zhigljavsky

2024

• Noonan, J. and Zhigljavsky, A. 2024. Improving exploration strategies in large dimensions and rate of convergence of global random search algorithms. Journal of Global Optimization 88, pp. 1-26. (10.1007/s10898-023-01308-6)

2023

• Pronzato, L. and Zhigljavsky, A. 2023. Quasi-uniform designs with optimal and near-optimal uniformity constant. Journal of Approximation Theory 294, article number: 105931. (10.1016/j.jat.2023.105931)
• Gillard, J., O'Riordan, E. and Zhigljavsky, A. 2023. Polynomial whitening for high-dimensional data. Computational Statistics 38, pp. 1427-1461. (10.1007/s00180-022-01277-6)
• Pronzato, L. and Zhigljavsky, A. 2023. BLUE against OLSE in the location model: energy minimization and asymptotic considerations. Statistical Papers 64, pp. 1187-1208. (10.1007/s00362-023-01423-2)
• Detter, H., Pepelyshev, A. and Zhigljavsky, A. 2023. Prediction in regression models with continuous observations. Statistical Papers (10.1007/s00362-023-01465-6)

2022

• Noonan, J. and Zhigljavsky, A. 2022. Random and quasi-random designs in group testing. Journal of Statistical Planning and Inference 221, pp. 29-54. (10.1016/j.jspi.2022.02.006)
• Zhigljavsky, A. 2022. Convergence of global random search algorithms. In: Pardalos, P. and Prokopyev, O. eds. Encylopedia of Optimization. Springer Nature, (10.1007/978-3-030-54621-2_769-1)
• Zhigljavsky, A. and Noonan, J. 2022. Covering of high-dimensional sets. In: Pardalos, P. and Prokopyev, O. eds. Encyclopedia of Optimization. Springer Nature, (10.1007/978-3-030-54621-2_770-1)
• Zhigljavsky, A. 2022. Random search for global optimization. In: Pardalos, P. and Prokopyev, O. eds. Encyclopedia of Optimization. Springer Nature, (10.1007/978-3-030-54621-2_768-1)
• Noonan, J. and Zhigljavsky, A. 2022. Efficient quantisation and weak covering of high dimensional cubes. Discrete and Computational Geometry 68, pp. 540-565. (10.1007/s00454-022-00396-7)
• Noonan, J. and Zhigljavsky, A. 2022. Non-lattice covering and quantization of high dimensional sets. In: Pardalos, P. M., Rasskazova, V. and Vrahatis, M. N. eds. Black Box Optimization, Machine Learning, and No-Free Lunch Theorems., Vol. 170. Springer Optimization and Its Applications New York: Springer, pp. 273-318., (10.1007/978-3-030-66515-9_10)
• Gillard, J., O'Riordan, E. and Zhigljavsky, A. 2022. Simplicial and minimal-variance distances in multivariate data analysis. Journal of Statistical Theory and Practice 16, article number: 9. (10.1007/s42519-021-00227-7)

2021

• Noonan, J. and Zhigljavsky, A. 2021. Approximations for the boundary crossing probabilities of moving sums of random variables. Methodology and Computing in Applied Probability 23, pp. 873-892. (10.1007/s11009-019-09769-7)
• Zhigljavsky, A. and Noonan, J. 2021. First passage times for Slepian process with linear and piecewise linear barriers. Extremes 24, pp. 565-589. (10.1007/s10687-021-00406-6)
• Zilinskas, A., Gillard, J., Scammell, M. and Zhigljavsky, A. 2021. Multistart with early termination of descents. Journal of Global Optimization 79, pp. 447-462. (10.1007/s10898-019-00814-w)
• Pronzato, L. and Zhigljavsky, A. 2021. Minimum-energy measures for singular kernels. Journal of Computational and Applied Mathematics 382, article number: 113089. (10.1016/j.cam.2020.113089)
• Noonan, J. and Zhigljavsky, A. 2021. Approximations for the boundary crossing probabilities of moving sums of normal random variables. Communications in Statistics - Simulation and Computation 50(11), pp. 3547-3568. (10.1080/03610918.2019.1626889)
• Dette, H. and Zhigljavsky, A. A. 2021. Reproducing kernel Hilbert spaces, polynomials, and the classical moment problem. SIAM/ASA Journal on Uncertainty Quantification 9(4), pp. 1589-1614. (10.1137/21M1394965)

2020

• Pepelyshev, A. and Zhigljavsky, A. 2020. Discrete uniform and binomial distributions with infinite support. Soft Computing 24, pp. 17517-17524. (10.1007/s00500-020-05190-2)
• Noonan, J. and Zhigljavsky, A. 2020. Power of the MOSUM test for online detection of a transient change in mean. Sequential Analysis 39(2), pp. 269-293. (10.1080/07474946.2020.1767406)
• Zhigljavsky, A. and Noonan, J. 2020. Covering of high-dimensional cubes and quantization. SN Operations Research Forum 1(3), article number: 18. (10.1007/s43069-020-0015-8)
• Pronzato, L. and Zhigljavsky, A. 2020. Bayesian quadrature, energy minimization, and space-filling design. SIAM/ASA Journal on Uncertainty Quantification 8(3), pp. 959-1011. (10.1137/18M1210332)
• Golyandina, N. and Zhigljavsky, A. 2020. Blind deconvolution of covariance matrix inverses for autoregressive processes. Linear Algebra and its Applications 593, pp. 188-211. (10.1016/j.laa.2020.02.005)

2019

• Noonan, J. and Zhigljavsky, A. 2019. Approximating Shepp's constants for the Slepian process. Statistics and Probability Letters 153, pp. 21-31. (10.1016/j.spl.2019.05.001)
• Dette, H., Pepelyshev, A. and Zhigljavsky, A. 2019. The BLUE in continuous-time regression models with correlated errors. Annals of Statistics 47(4), pp. 1928-1959. (10.1214/18-AOS1734)
• Pronzato, L. and Zhigljavsky, A. 2019. Measures minimizing regularized dispersion. Journal of Scientific Computing 78(3), pp. 1550-1570. (10.1007/s10915-018-0817-4)
• Phillips, T. R. L., Schmidt, K. M. and Zhigljavsky, A. 2019. Extension of the Schoenberg theorem to integrally conditionally positive definite functions. Journal of Mathematical Analysis and Applications 470(1), pp. 659-678. (10.1016/j.jmaa.2018.10.032)

2018

• Noonan, J. and Zhigljavsky, A. 2018. Approximations of the boundary crossing probabilities for the maximum of moving weighted sums. Statistical Papers 59(4), pp. 1325-1337. (10.1007/s00362-018-1015-z)
• Gillard, J. and Zhigljavsky, A. 2018. Optimal directional statistic for general regression. Statistics and Probability Letters 143, pp. 74. (10.1016/j.spl.2018.07.025)
• Pronzato, L., Wynn, H. P. and Zhigljavsky, A. A. 2018. Simplicial variances, potentials and Mahalanobis distances. Journal of Multivariate Analysis 168, pp. 276-289. (10.1016/j.jmva.2018.08.002)
• Pepelyshev, A., Zhigljavsky, A. and Zilinskas, A. 2018. Performance of global random search algorithms for large dimensions. Journal of Global Optimization 71, pp. 57-71. (10.1007/s10898-017-0535-8)
• Gillard, J. and Zhigljavsky, A. 2018. Optimal estimation of direction in regression models with large number of parameters. Applied Mathematics and Computation 318, pp. 281-289. (10.1016/j.amc.2017.05.050)

2017

• Pronzato, L., Wynn, H. P. and Zhigljavsky, A. 2017. Extended generalised variances, with applications. Bernoulli 23(4A), pp. 2617-2642. (10.3150/16-BEJ821)
• Dette, H., Konstantinou, M. and Zhigljavsky, A. 2017. A new approach to optimal designs for correlated observations. Annals of Statistics 45(4), pp. 1579-1608. (10.1214/16-AOS1500)
• Pepelyshev, A. and Zhigljavsky, A. 2017. SSA analysis and forecasting of records for Earth temperature and ice extents. Statistics and Its Interface 10(1), pp. 151-163. (10.4310/SII.2017.v10.n1.a14)
• Pepelyshev, A., Kornikov, V. and Zhigljavsky, A. 2017. Statistical estimation in global random search algorithms in case of large dimensions. Lecture Notes in Computer Science 10556, pp. 364-369. (10.1007/978-3-319-69404-7_32)

2016

• Pronzato, L., Wynn, H. P. and Zhigljavsky, A. 2016. Extremal measures maximizing functionals based on simplicial volumes. Statistical Papers 57, pp. 1059-1075. (10.1007/s00362-016-0767-6)
• Pepelyshev, A., Staroselskiy, Y., Zhigljavsky, A. and Guchenko, R. 2016. Adaptive targeting in online advertisement: models based on relative influence of factors. Presented at: International Workshop on Machine Learning, Optimization and Big Data, Volterra, Italy, 26-29 August 2016 Presented at Pardalos, P. et al. eds.Machine Learning, Optimization, and Big Data. MOD 2016, Vol. 10122. Lecture Notes in Computer Science Springer pp. 159-169., (10.1007/978-3-319-51469-7_13)
• Davies, G., Gillard, J. and Zhigljavsky, A. 2016. Comparative study of different penalty functions and algorithms in survey calibration. In: Comparative Study of Different Penalty Functions and Algorithms in Survey Calibration., Vol. 107., pp. 87-127., (10.1007/978-3-319-29975-4_6)
• Gillard, J. W. and Zhigljavsky, A. A. 2016. Weighted norms in subspace-based methods for time series analysis. Numerical Linear Algebra with Applications 23(5), pp. 947-967. (10.1002/nla.2062)
• Dette, H., Pepelyshev, A. and Zhigljavsky, A. 2016. Optimal designs for regression models with autoregressive errors. Statistics and Probability Letters 116, pp. 107-115. (10.1016/j.spl.2016.04.008)
• Pepelyshev, A., Staroselskiy, Y. and Zhigljavsky, A. A. 2016. Adaptive designs for optimizing online advertisement campaigns. Presented at: 11th International Workshop in Model-Oriented Design and Analysis, Hamminkeln, Germany, 12-17 June 2016 Presented at Kunert, J., Muller, C. H. and Atkinson, A. C. eds.mODa 11 - Advances in Model-Oriented Design and Analysis: Proceedings of the 11th International Workshop in Model-Oriented Design and Analysis held in Hamminkeln, Germany, June 12-17, 2016. Contributions to Statistics Springer Verlag pp. 199-208., (10.1007/978-3-319-31266-8_23)
• Z̆ilinskas, A. and Zhigljavsky, A. A. 2016. Branch and probability bound methods in multi-objective optimization. Optimization Letters 10(2), pp. 341-353. (10.1007/s11590-014-0777-z)
• Dette, H., Pepelyshev, A. and Zhigljavsky, A. 2016. Optimal designs in regression with correlated errors. Annals of Statistics 44(1), pp. 113-152. (10.1214/15-AOS1361)
• Pepelyshev, A., Staroselskiy, Y. and Zhigljavsky, A. 2016. Adaptive targeting for online advertisement. Presented at: International Workshop on Machine Learning, Optimization and Big Data, Taormina, Italy, 21-23 July 2015 Presented at Pardalos, P. et al. eds.Machine Learning, Optimization, and Big Data: First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers, Vol. 9432. Lecture Notes in Computer Science Springer Verlag pp. 240-251., (10.1007/978-3-319-27926-8_21)
• Gillard, J. W. and Zhigljavsky, A. 2016. Global optimization for structured low rank approximation. Presented at: International Conference of Numerical Analysis and Applied Mathematics 2015, Rhodes, Greece, 22-28 September 2015AIP Conference Proceedings, Vol. 1738. American Institute of Physics pp. 400003., (10.1063/1.4952191)

2015

• Dette, H., Pepelyshev, A. and Zhigljavsky, A. 2015. Design for linear regression models with correlated errors. In: Dean, A. et al. eds. Handbook of Design and Analysis of Experiments. Chapman & Hall/CRC Handbooks of Modern Statistical Methods CRC Press, pp. 236-276.
• Gillard, J. W. and Zhigljavsky, A. A. 2015. Stochastic algorithms for solving structured low-rank approximation problems. Communications in Nonlinear Science and Numerical Simulation 21(1-3), pp. 70-88. (10.1016/j.cnsns.2014.08.023)
• Davies, G. P., Gillard, J. W. and Zhigljavsky, A. A. 2015. Calibration in survey sampling as an optimization problem. In: Migdalas, A. and Karakitsiou, A. eds. Optimization, Control, and Applications in the Information Age., Vol. 130. Springer Proceedings in Mathematics & Statistics Vol. 130. Springer, pp. 67-89., (10.1007/978-3-319-18567-5_4)

2014

• Pronzato, L. and Zhigljavsky, A. A. 2014. Algorithmic construction of optimal designs on compact sets for concave and differentiable criteria. Journal of Statistical Planning and Inference 154, pp. 141-155. (10.1016/j.jspi.2014.04.005)
• Dette, H., Pepelyshev, A. and Zhigljavsky, A. 2014. ‘Nearly’ universally optimal designs for models with correlated observations. Computational Statistics & Data Analysis 71, pp. 1103-1112. (10.1016/j.csda.2013.02.002)

2013

• Schmidt, K. M. and Zhigljavsky, A. A. 2013. An extremal property of the generalized arcsine distribution. Metrika 76(3), pp. 347-355. (10.1007/s00184-012-0391-y)
• Dette, H., Pepelyshev, A. and Zhigljavsky, A. A. 2013. Optimal design for linear models with correlated observations. Annals of Statistics 41(1), pp. 143-176. (10.1214/12-AOS1079)
• Hassani, H., Heravi, S. and Zhigljavsky, A. A. 2013. Forecasting UK industrial production with multivariate singular spectrum analysis. Journal of Forecasting 32(5), pp. 395-408. (10.1002/for.2244)
• Gillard, J. W. and Zhigljavsky, A. A. 2013. Optimization challlenges in the structured low rank approximation problem. Journal of Global Optimization 57(3), pp. 733-751. (10.1007/s10898-012-9962-8)
• Zhigljavsky, A., Pronzato, L. and Bukina, E. 2013. An asymptotically optimal gradient algorithm for quadratic optimization with low computational cost. Optimization Letters 7(6), pp. 1047-1059. (10.1007/s11590-012-0491-7)

2012

• Pronzato, L., Zhigljavsky, A. and Bukina, A. 2012. Estimation of spectral bounds in gradient algorithms. Acta Applicandae Mathematicae 127(1), pp. 117-136. (10.1007/s10440-012-9794-z)

2011

• Hassani, H., Xu, Z. and Zhigljavsky, A. A. 2011. Singular spectrum analysis based on the perturbation theory. Nonlinear Analysis: Real World Applications 12(5), pp. 2752-2766. (10.1016/j.nonrwa.2011.03.020)
• Pronzato, L. and Zhigljavsky, A. A. 2011. Gradient algorithms for quadratic optimization with fast convergence rates. Computational Optimization and Applications 50(3), pp. 597-617. (10.1007/s10589-010-9319-5)
• Gillard, J. W. and Zhigljavsky, A. A. 2011. Analysis of structured low rank approximation as an optimization problem. Informatica 22(4), pp. 489-505.
• Patterson, K., Hassani, H., Heravi, S. and Zhigljavsky, A. A. 2011. Multivariate singular spectrum analysis for forecasting revisions to real-time data. Journal of Applied Statistics 38(10), pp. 2183-2211. (10.1080/02664763.2010.545371)
• Zhigljavsky, A. A. 2011. Statistical Modelling in Market Research. In: Lovric, M. ed. International Encyclopedia of Statistical Science. Springer Reference Berlin: Springer, pp. 1450-1452., (10.1007/978-3-642-04898-2_548)

2010

• Zhigljavsky, A. A., Dette, H. and Pepelyshev, A. 2010. A new approach to optimal design for linear models with correlated observations. Journal of the American Statistical Association 105(491), pp. 1093-1103. (10.1198/jasa.2010.tm09467)
• Hassani, H., Soofi, A. and Zhigljavsky, A. A. 2010. Predicting daily exchange rate with singular spectrum analysis. Nonlinear Analysis: Real World Applications 11(3), pp. 2023-2034. (10.1016/j.nonrwa.2009.05.008)
• Zhigljavsky, A. A. 2010. Nonadaptive group testing with lies: Probabilistic existence theorems. Journal of Statistical Planning and Inference 140(10), pp. 2825-2893. (10.1016/j.jspi.2010.03.012)
• Zhigljavsky, A. A. and Hamilton, E. 2010. Stopping rules in k-adaptive global random search algorithms. Journal of Global Optimization 48(1), pp. 87-97. (10.1007/s10898-010-9528-6)
• Zhigljavsky, A. A. 2010. Singular Spectrum Analysis for time series: Introduction to this special issue. Statistics and Its Interface 3(3), pp. 255-258.
• Pepelyshev, A. and Zhigljavsky, A. A. 2010. Assessing the stability of long-horizon SSA forecasting. Statistics and Its Interface 3(3), pp. 321-327.

2009

• Hassani, H., Heravi, S. and Zhigljavsky, A. A. 2009. Forecasting European industrial production with singular spectrum analysis. International Journal of Forecasting 25(1), pp. 103-118. (10.1016/j.ijforecast.2008.09.007)
• Schmidt, K. M. and Zhigljavsky, A. A. 2009. A characterization of the arcsine distribution. Statistics & Probability Letters 79(24), pp. 2451-2455. (10.1016/j.spl.2009.08.018)
• Hassani, H. and Zhigljavsky, A. A. 2009. Singular spectrum analysis: methodology and application to economics data. Journal of Systems Science and Complexity 22(3), pp. 372-394. (10.1007/s11424-009-9171-9)
• Dette, H., Leonenko, N. N., Pepelyshev, A. and Zhigljavsky, A. A. 2009. Asymptotic optimal designs under long-range dependence error structure. Bernoulli 15(4), pp. 1036-1056. (10.3150/09-BEJ185)

2008

• Dette, H., Pepelyshev, A. and Zhigljavsky, A. A. 2008. Improving updating rules in multiplicative algorithms for computing D-optimal designs. Computational Statistics & Data Analysis 53(2), pp. 312-320. (10.1016/j.csda.2008.10.002)

2007

• Savani, V. and Zhigljavsky, A. A. 2007. Asymptotic distributions of statistics and parameter estimates for mixed Poisson processes. Journal of Statistical Planning and Inference 137(12), pp. 3990-4002. (10.1016/j.jspi.2007.04.016)
• Leonenko, N. N., Savani, V. and Zhigljavsky, A. A. 2007. Autoregressive negative binomial processes. Annales de l'Institut de Statistique de l'Universite de Paris 51(1), pp. 25-47.
• Fedorov, V., Jones, B., Savani, V. and Zhigljavsky, A. A. 2007. Deriving approximations in a random effects model for multicentre clinical trials with binary response. Communications in Statistics - Theory and Methods 36(3), pp. 629-644. (10.1080/03610920601001832)

2006

• Savani, V. and Zhigljavsky, A. A. 2006. Efficient parameter estimation for independent and INAR(1) negative binomial samples. Metrika 65(2), pp. 207-225. (10.1007/s00184-006-0071-x)
• Pronzato, L., Wynn, H. P. and Zhigljavsky, A. A. 2006. Asymptotic behaviour of a family of gradient algorithms in Rd and Hilbert spaces. Mathematical Programming 107(3), pp. 409-438. (10.1007/s10107-005-0602-7)
• Savani, V. and Zhigljavsky, A. A. 2006. Efficient Estimation of Parameters of the Negative Binomial Distribution. Communications in Statistics - Theory and Methods 35(5), pp. 767-783. (10.1080/03610920500501346)

2004

• Moshchevitin, N. and Zhigljavsky, A. A. 2004. Entropies of the partitions of the unit interval generated by the Farey tree. Acta Arithmetica 115(1), pp. 47-58.

2003

• Zhigljavsky, A. A. 2003. Probabilistic existence theorems in group testing. Journal of Statistical Planning and Inference 115(1), pp. 1-43. (10.1016/S0378-3758(02)00148-9)

2001

• Golyandina, N., Nekrutkin, V. V. and Zhigljavsky, A. A. 2001. Analysis of time series structure: SSA and related techniques. Monographs on Statistics & Applied Probability. Chapman & Hall/ CRC.

1999

• Aliev, I. and Zhigljavsky, A. 1999. Weyl sequences: asymptotic distributions of the partition lengths. Acta Arithmetica 88(4), pp. 351-361.