Research Works

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 LogNormals', Annals of Operations Research, http://dx.doi.org/10.1007/s1047901903161x

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. 2331, 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 48, 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, YiLung 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 Lognormal 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, 25th28th Oct, 2016,
http://dx.doi.org/10.4108/eai.25102016.2266879
 Z. I. Botev and Ad Ridder (2016), An Mestimator for rareevent 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. 125148,
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 Studentt 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/s1122201595469

Z. I. Botev, C. J. Lloyd (2015), Importance accelerated RobbinsMonro algorithm
with applications to parametric confidence limits,
Electronic Journal of Statistics, Volume 9, Number 2, pages 20582075,
http://dx.doi.org/10.1214/15EJS1071

Z. I. Botev, A. Ridder, L. RojasNandayapa (2016), Semiparametric Cross Entropy For RareEvent
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 MarshallOlkin 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 JC (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, SpringerVerlag, Berlin, pp. 369  404,
http://dx.doi.org/10.1007/9783319100647_12

Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2012),
Dependent Failures in HighlyReliable 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/s1122201193082

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 CrossEntropy 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 OneStep LookAhead 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/s1122201092014

Botev. Z.I., Grotowski J.F and Kroese D. P. (2010). Kernel density estimation via diffusion. Annals of Statistics. Volume 38, Number 5, Pages 29162957

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/s1100900991337

Botev, Z.I., Kroese, D.P. (2008).
Nonasymptotic bandwidth selection for density estimation of discrete data. Methodology and Computing in Applied Probability.
Volume 10, Number 3, 435451,

Botev, Z.I., Kroese, D.P. (2008). An Efficient Algorithm for
Rareevent Probability Estimation, Combinatorial Optimization, and
Counting. Methodology and Computing in Applied
Probability. Volume 10, Number 4, 471505
(pdf)

D. P. Kroese, T. Taimre, Z. I. Botev and R. Y. Rubinstein (2007),
Solutions Manual for Monte Carlo Methods,
WileyInterscience, Second edition
[
Wiley 
Amazon 
Barnes and Noble ]

Botev, Z.I, Kroese, D.P., Taimre, T. (2007).
Generalized CrossEntropy Methods with Applications to RareEvent simulation and Optimization.
Simulation. Volume 83, Number 11, 785806

Botev, Z.I, Kroese, D.P., Taimre, T. (2006). Generalized CrossEntropy
Methods.
Proceedings of RESIM 2006, Bamberg, Germany, 130. (pdf)

Botev, Z., Kroese, D.P. (2004).
Global Likelihood Optimization via the CrossEntropy Method,
with an Application to Mixture Models. Proceedings of the Winter
Simulation Conference, Washington DC, pp 529535.
(pdf)

Bachelor of Science Honors Project

This paper has been used to help write the kernel density estimation software.
