Truncated Multivariate Student & Normal Toolbox

Statistical Software/Logiciels d'analyse Statistique

Research Works

• S. Macnamara, E. Schlögl and Z. I. Botev (2021), Estimation When Both Covariance And Precision Matrices Are Sparse, submitted
• A. Mathur, S. Moka and Z. I. Botev (2021), Variance Reduction for Matrix Computations with Applications to Gaussian Processes, submitted
• M. Colbrook, Z. I. Botev, K. Kuritz and S. MacNamara (2020), Kernel Density Estimation with Linked Boundary Conditions, Studies in Applied Mathematics, http://dx.doi.org/10.1111/sapm.12322
• Z. I. Botev, P. L'Ecuyer (2020), Sampling Conditionally on a Rare Event via Generalized Splitting, INFORMS Journal on Computing, Article number: ijoc.2019.0936, https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2019.0936
• N. B. Rached, D. Mackinley, Z. I. Botev, R. Tempone, S. Alouini (2020), A Universal Splitting Estimator for the Performance Evaluation of Wireless Communications Systems, IEEE Transactions on Wireless Communications, pp. 1 - 10, http://dx.doi.org/10.1109/TWC.2020.2982649
• D. P. Kroese, Z. I. Botev, T. Taimre, S. Vaisman (2019), Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman & Hall/CRC Press, Book
• T. Taimre, D. P. Kroese, and Z. I. Botev (2019), Monte Carlo Methods, Wiley StatsRef: Statistical Reference Online. John Wiley & Sons, Ltd.,http://dx.doi.org/10.1002/9781118445112.stat03619.pub2
• Z. Botev; R. Salomone; D. Mackinlay, (2019), 'Fast and Accurate Computation of the Distribution of Sums of Dependent Log-Normals', Annals of Operations Research, http://dx.doi.org/10.1007/s10479-019-03161-x
• Patrick J. Laub; Robert Salomone; Zdravko I. Botev, (2019), 'Monte Carlo estimation of the density of the sum of dependent random variables', Mathematics and Computers in Simulation, Vol. 161, July 2019, pp. 23-31, http://dx.doi.org/10.1016/j.matcom.2018.12.001
• D. Mackinlay and Z. Botev, (2019), 'Mosaic Style Transfer Using Sparse Autocorrelograms', in Proceedings of the 20th ISMIR Conference, Delft, Netherlands, November 4-8, 2019, Delft, The Netherlands, presented at 20th annual conference of the International Society for Music Information Retrieval (ISMIR), Delft, The Netherlands, 04 November 2019 - 08 November 2019, https://ismir2019.ewi.tudelft.nl/?q=node/1
• Z. I. Botev, Yi-Lung Chen, P. L'Ecuyer, S. MacNamara and D. P. Kroese (2018), Exact Posterior Simulation From The Linear Lasso Regression, Proceedings of the 2018 Winter Simulation Conference, pp. 1706 - 1717, Gothenburg, Sweden, 09 December 2018 - 12 December 2018, http://dx.doi.org/10.1109/WSC.2018.8632237
• P. L'Ecuyer, Z. I. Botev and D. P. Kroese (2018), On a Generalzied Splitting Method For Sampling From a Conditional Distribution, Proceedings - 2018 Winter Simulation Conference, pp. 1694 - 1705, Gothenburg, Sweden, 09 December 2018 - 12 December 2018, http://dx.doi.org/10.1109/WSC.2018.8632422
• Z. I. Botev and P. L'Ecuyer (2017), Simulation from the Tail of the Univariate and Multivariate Normal Distribution, in Innovations in Communications and Computing, edited by A. Puliafito and K. Trivedi, Chapter 8, Springer
• Z. I. Botev, A. Ridder (2017), Variance Reduction , in Balakrishnan N; Colton T; Everitt B; Piegorsch W; Ruggeri F; Teugels JL (ed.), Wiley StatsRef: Statistics Reference Online, Wiley Online Library, pp. 1 - 6, http://dx.doi./10.1002/9781118445112.stat07975
• N. B. Rached, Z. I. Botev, A. Kammoun, S. Alouini, R. Tempone (2018), Importance sampling estimator of outage probability under generalized selection combining model, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 3909 - 3913, Calgary, AB, Canada, 15 April - 20 April 2018, http://dx.doi.org/10.1109/ICASSP.2018.8462177
• N. B. Rached, Z. I. Botev, A. Kammoun, S. Alouini, R. Tempone (2018), On the Sum of Order Statistics and Applications to Wireless Communication Systems Performances, IEEE Transactions on Wireless Communications, vol. 17, pp. 7801 - 7813, http://dx.doi.org/10.1109/TWC.2018.2871201
• Z. I. Botev and P. L'Ecuyer (2017), Accurate Computation of The Right Tail of The Sum of Dependent Log-normal variates, in Chan WKV; D'Ambrogio A; Zacharewicz G; Mustafee N; Wainer G; Page E (eds.), 2017 Winter Simulation Conference (WSC), IEEE, pp. 1880 - 1890, Las Vegas, NV, USA, 03 December - 06 December 2017, http://dx.doi.org/10.1109/WSC.2017.8247924
• Z. I. Botev and D. Mackinlay and Y.-L. Chen (2017), Logarithmically Efficient Estimation of the Tail of the Multivariate Normal Distribution, in Chan WKV; D'Ambrogio A; Zacharewicz G; Mustafee N; Wainer G; Page E (eds.), 2017 Winter Simulation Conference (WSC), IEEE, pp. 1903 - 1913, Las Vegas, NV, USA, 03 December - 06 December 2017, http://dx.doi.org/10.1109/WSC.2017.8247926
• Z. I. Botev and P. L'Ecuyer (2016), Simulation from the Normal Distribution Truncated to an Interval in the Tail, in ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools, pp. 23 - 29, Taormina, Italy, 25th-28th Oct, 2016, http://dx.doi.org/10.4108/eai.25-10-2016.2266879
• Z. I. Botev and Ad Ridder (2016), An M-estimator for rare-event probability estimation, in T. M. K. Roeder, P. I. Frazier, R. Szechtman, and E. Zhou, eds. Proceedings of the 2016 Winter Simulation Conference, IEEE Press Piscataway, pp. 359 - 369, Arlington, Virginia, USA, 11 December - 14 December 2016, http://dx.doi.org/10.1109/WSC.2016.7822103
• Z. I. Botev (2016), The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting, Journal of the Royal Statistical Society, Series B (Statistical Methodology), Vol. 79, pp. 125-148, http://dx.doi.org/10.1111/rssb.12162
• Z. I. Botev, M. Mandjes and A. Ridder (2015), Tail distribution of the Maximum of Correlated Gaussian Random Variables, in Yilmaz, L; Chan WKV; Moon I; Roeder TMK; Macal C; Rossetti, MD (eds.), Proceedings of the 2015 Winter Simulation Conference, IEEE, pp. 633 - 642, Huntington Beach, CA, 06 December 2015 - 09 December 2015, http://dx.doi.org/10.1109/WSC.2015.7408202
• Z. I. Botev and P. L'Ecuyer (2015), Efficient Estimation and Simulation of the Truncated Multivariate Student-t Distribution, in Yilmaz, L; Chan WKV; Moon I; Roeder TMK; Macal C; Rossetti, MD (eds.), Proceedings of the 2015 Winter Simulation Conference, IEEE, pp. 380 - 391, Huntington Beach, CA, 06 December 2015 - 09 December 2015, http://dx.doi.org/10.1109/WSC.2015.7408180
• R. Vaisman, Z. I. Botev, A. Ridder (2015), Sequential Monte Carlo for Counting Vertex Covers in General Graphs, Statistics and Computing, vol. 26, pp. 591 - 607, http://dx.doi.org/10.1007/s11222-015-9546-9
• Z. I. Botev, C. J. Lloyd (2015), Importance accelerated Robbins-Monro algorithm with applications to parametric confidence limits, Electronic Journal of Statistics, Volume 9, Number 2, pages 2058-2075, http://dx.doi.org/10.1214/15-EJS1071
• Z. I. Botev, A. Ridder, L. Rojas-Nandayapa (2016), Semiparametric Cross Entropy For Rare-Event Simulation, Journal of Applied Probability ,Volume 53, pp. 633 - 649, http://dx.doi.org/10.1017/jpr.2016.31
• Z. I. Botev, P. L'Ecuyer, R. Simard, B. Tuffin (2015), Static Network Reliability Estimation Under the Marshall-Olkin Copula, ACM Transactions on Modeling and Computer Simulation, vol. 26, http://dx.doi.org/10.1145/2775106
• Z. I. Botev, S. Vaisman, R. Y. Rubinstein, P. L'Ecuyer (2014), Reliability of Stochastic Flow Networks with Continuous Link Capacities, in Tolk A; Diallo SY; Ryzhov IO; Yilmaz L; Buckley S; Miller JA (eds.), Proceedings of the 2014 Winter Simulation Conference, IEEE, pp. 543 - 552, Savannah, Georgia, USA, 07 December - 10 December 2014, http://dx.doi.org/10.1109/WSC.2014.7019919
• D. P. Kroese, T. Brereton, T. Taimre, Z. I. Botev (2014), Why the Monte Carlo Method is so important today Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 6, pp. 386 - 392, http://dx.doi.org/10.1002/wics.1314
• Z. Botev, P. L'Ecuyer, and B. Tuffin (2013), Modeling and Estimating Small Unreliabilities for Static Networks with Dependent Components, in Caruge D; Calvin C; Diop CM; Malvagi F; Trama J-C (eds.), Proceedings of SNA&MC 2013: Supercomputing in Nuclear Applications and Monte Carlo, Paris, France, 27 October - 31 October 2013, pp. Article #03306, http://dx.doi.org/10.1051/snamc/201403306
• D. P. Kroese and Z. I. Botev (2013), Spatial Process Generation, in Schmidt V (ed.), Lectures on Stochastic Geometry, Spatial Statistics and Random Fields, Volume II: Analysis, Modeling and Simulation of Complex Structures, Springer-Verlag, Berlin, pp. 369 - 404, http://dx.doi.org/10.1007/978-3-319-10064-7_12
• Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2012), Dependent Failures in Highly-Reliable Static Networks, Proceedings of the 2012 Winter Simulation Conference, IEEE Press, Winter Simulation Conference, Berlin, Germany, pp. Article 39 (12pp), 09 December - 12 December 2012, http://dx.doi.org/10.1109/WSC.2012.6465033
• Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2012), Markov chain importance sampling with applications to rare event probability estimation, Statistics and Computing, , vol. 23, pp. 271 - 285, http://dx.doi.org/10.1007/s11222-011-9308-2
• Z. I. Botev, P. L'Ecuyer, G. Rubino, R. Simard, and B. Tuffin (2012), Static Network Reliability Estimation via Generalized Splitting, INFORMS Journal on Computing, vol. 25, pp. 56 - 71, http://dx.doi.org/10.1287/ijoc.1110.0493
• Z. I. Botev, D. P. Kroese, R. Y. Rubinstein, and P. L'Ecuyer (2012), The Cross-Entropy Method for Optimization, In Handbook of Statistics , Volume 31: Machine Learning. V. Govindaraju and C.R. Rao, Eds, North Holland.
• Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2011), An Importance Sampling Method Based on a One-Step Look-Ahead Density from a Markov Chain, Proceedings of the 2011 Winter Simulation Conference, IEEE Press,
• D. P. Kroese, T. Taimre and Z. I. Botev (2011), Handbook of Monte Carlo Methods, John Wiley & Sons , [ Wiley, homepage]
• Botev. Z.I. and Kroese D. P. (2010). Efficient Monte Carlo simulation via the Generalized Splitting Method. Statistics and Computing. DOI: 10.1007/s11222-010-9201-4
• Botev. Z.I., Grotowski J.F and Kroese D. P. (2010). Kernel density estimation via diffusion. Annals of Statistics. Volume 38, Number 5, Pages 2916--2957
• Botev, Z.I., Kroese, D.P. (2009). The Generalized Cross Entropy Method, with Applications to Probability Density Estimation. Methodology and Computing in Applied Probability. DOI: 10.1007/s11009-009-9133-7
• Botev, Z.I., Kroese, D.P. (2008). Non-asymptotic bandwidth selection for density estimation of discrete data. Methodology and Computing in Applied Probability. Volume 10, Number 3, 435-451,
• Botev, Z.I., Kroese, D.P. (2008). An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting. Methodology and Computing in Applied Probability. Volume 10, Number 4, 471-505 (pdf)
• D. P. Kroese, T. Taimre, Z. I. Botev and R. Y. Rubinstein (2007), Solutions Manual for Monte Carlo Methods, Wiley-Interscience, Second edition [ Wiley | Amazon | Barnes and Noble ]
• Botev, Z.I, Kroese, D.P., Taimre, T. (2007). Generalized Cross-Entropy Methods with Applications to Rare-Event simulation and Optimization. Simulation. Volume 83, Number 11, 785-806
• Botev, Z.I, Kroese, D.P., Taimre, T. (2006). Generalized Cross-Entropy Methods. Proceedings of RESIM 2006, Bamberg, Germany, 1-30. (pdf)
• Botev, Z., Kroese, D.P. (2004). Global Likelihood Optimization via the Cross-Entropy Method, with an Application to Mixture Models. Proceedings of the Winter Simulation Conference, Washington DC, pp 529--535. (pdf)
• Bachelor of Science Honors Project
• This paper has been used to help write the kernel density estimation software.

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