Research Works
 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, Wiley StatsRef: Statistics Reference Online: pages 16. doi:10.1002/9781118445112.stat07975

Z. I. Botev, R. Salomone, and D. Mackinlay (2018), Accurate Computation of the Distribution of Sums of
Dependent LogNormals with Applications to the BlackScholes
Model, submitted

R. Salomone, P. J. Laub, and Z. I. Botev (2018), Monte Carlo Estimation of the Density of the Sum of
Dependent Random Variables, submitted

N. B. Rached, Z. I. Botev, A. Kammoun, S. Alouini, R. Tempone (2018), Importance sampling estimator of
outage probability under generalized selection combining model, submitted

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, submitted
 Z. I. Botev and P. L'Ecuyer (2017), Accurate Computation of The Right Tail of The Sum of Dependent Lognormal variates,
Proceedings of the 2017 Winter Simulation Conference
 Z. I. Botev and D. Mackinlay and Y.L. Chen (2017), Logarithmically Efficient Estimation of the Tail
of the Multivariate Normal Distribution, Proceedings of the 2017 Winter Simulation Conference
 Z. I. Botev and P. L'Ecuyer (2016), Simulation from the Normal Distribution
Truncated to an Interval in the Tail, Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools
, Taormina Italy, 2016
 Z. I. Botev and Ad Ridder (2016), An Mestimator for rareevent probability estimation,
Proceedings of the 2016 Winter Simulation Conference , (T. M. K. Roeder, P. I. Frazier, R. Szechtman, and E. Zhou, eds.)

Z. I. Botev (2017), The Normal Law Under Linear Restrictions: Simulation
and Estimation via Minimax Tilting,
Journal of the Royal Statistical Society, Series B , Volume 79, Part 1, pp. 124

Z. I. Botev and M. Mandjes and A. Ridder (2015), Tail distribution of the Maximum of Correlated Gaussian Random Variables,
Proceedings of the 2015 Winter Simulation Conference

Z. I. Botev and P. L'Ecuyer (2015), Efficient Estimation and Simulation of the Truncated Multivariate Studentt Distribution,
Proceedings of the 2015 Winter Simulation Conference

R. Vaisman, Z. I. Botev, A. Ridder (2015), Sequential Monte Carlo for Counting Vertex Covers in General Graphs,
Statistics and Computing, DOI :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.

Z. I. Botev, A. Ridder, L. RojasNandayapa (2016), Semiparametric Cross Entropy For RareEvent
Simulation,
Journal of Applied Probability
, Volume 53, Number 3

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

Z. I. Botev, S. Vaisman, R. Y. Rubinstein, P. L'Ecuyer (2014), Reliability of Stochastic Flow Networks with Continuous Link Capacities,
Proceedings of the 2014 Winter Simulation Conference,
A Tolks, S.D. Diallo, I.O, Ryzhov, L. Yilmaz, S. Buckley, and J.A. Miller, eds.

D. P. Kroese, T. Brereton, T. Taimre, Z. I. Botev (2014),
Why the Monte Carlo Method is so important today.
To appear in Wiley Interdisciplinary Reviews: Computational Statistics

Z. Botev, P. L'Ecuyer, and B. Tuffin (2013), Modeling and Estimating Small Unreliabilities for
Static Networks with Dependent Components , Proceedings of SNA&MC 2013: Supercomputing
in Nuclear Applications and Monte Carlo, article #03306, DOI: http://dx.doi.org/10.1051/snamc/201403306.

D. P. Kroese and Z. I. Botev (2013),
Spatial Process Generation, To appear in: V. Schmidt (Ed.).
Lectures on Stochastic Geometry, Spatial Statistics and Random Fields,
Volume II: Analysis, Modeling and Simulation of Complex Structures,
SpringerVerlag, Berlin

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

Z. I. Botev, P. L'Ecuyer, and B. Tuffin (2012),
Markov chain importance sampling with applications to rare event probability estimation,
Statistics and Computing, to appear, doi: 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, to appear, doi: 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.
