Papers
Duan, H., Ökten, G. (2025) Derivative-based Shapley value for
global sensitivity analysis and machine learning explainability,
International Journal for Uncertainty Quantification, 15(1), pp 1-16.
Ökten, G. (2024) Number Sequences for Simulation, In “Diophantine problems: Determinism, Randomness,
and Applications", Panoramas et Synthèses, Eds. Kreso, D., Rivat, J., Tichy, F. R. Société
Mathématique de France, pp. 161–190.
Duan, H., Ökten, G. (2023) Control
variate Monte Carlo estimators based on sparse polynomial chaos expansion.
Socio-Environmental Systems Modelling, Vol 5, 18568. Special issue: Sensitivity Analysis of Model Output.
Yue, R., Duan, H., Uzunoğlu, B., Ökten, G. (2022) A comparison of global sensitivity methods for power systems.
In IEEE ICSRS 2022 6th International Conference on System Reliability and Safety, November 23-25, 2022, Venice, Italy.
Salehy, N., Ökten, G. (2022)
Monte Carlo and quasi-Monte Carlo methods for Dempster’s rule of combination.
International Journal of Approximate Reasoning, Vol 145, pp 163-186. https://doi.org/10.1016/j.ijar.2022.03.008
Chen, Y., Ökten, G. (2022) A goodness-of-fit test for copulas based on the collision test.
Statistical Papers, 63(5), 1369-1385.
Salehy, N., Ökten, G. (2021) Dempster-Shafer Theory for Stock Selection.
IEEE 45th Annual
Computers, Software, and Applications Conference (COMPSAC), July 12-16, 2021.
Fox, J., Ökten, G. (2021) Polynomial Chaos as a Control Variate Method. SIAM Journal on Scientific
Computing, 43(3), pp A2268-A2294.
Fox, J., Ökten, G. (2021) Brownian Path Generation with Polynomial Chaos, SIAM Journal on Financial
Mathematics, 12(2), pp 724-743.
Ökten, G., Liu, Y. (2021) Randomized quasi-Monte Carlo methods in global sensitivity analysis. Reliability Engineering &
System Safety, Volume 210, 107520, ISSN 0951-8320.
Polala, A., Ökten, G. (2020) Implementing de-biased estimators using mixed sequences.
Monte Carlo Methods and Applications, 26 (4), 293-301.
Mandel, D., Ökten, G. (2020) Randomized Global Sensitivity
Analysis and Model Robustness. In: Tuffin B., L'Ecuyer P. (eds) Monte Carlo and Quasi-Monte Carlo Methods. MCQMC 2018. Springer Proceedings in Mathematics & Statistics, vol 324, pp 403-421. Springer, Cham.
Fox, J., Ökten, G., Uzunoğlu, B. (2019) Global Sensitivity Analysis for Power Systems via Quasi-Monte Carlo Methods. In: IEEE ICSRS 2019 4th International Conference on System Reliability and Safety,
November 20-22, 2019, Rome, Italy.
Cellat, S., Fan, Y., Mio, W., Ökten, G. (2019). Learning Shape Metrics
with Monte Carlo Optimization. Journal of Computational and
Applied Mathematics, 348, 120-129.
Nguyen, N., Xu, L., Ökten, G. (2018). A Quasi-Monte Carlo
Implementation of the Ziggurat Method. Monte Carlo Methods and
Applications, 24 2, 93-99.
Tzeng, Y., Beaumont, P., Ökten, G. (2018). Time Series Simulation
with Randomized Quasi-Monte Carlo Methods: An Application to Value
at Risk and Expected Shortfall. Computational Economics, 52 1, 55-77.
Mandel, D., Ökten, G. (2018). Randomized Sobol' Sensitivity Indices. In Art Owen,
Peter W. Glynn (Eds.), Monte Carlo and Quasi-Monte Carlo Methods,
Springer Proceedings in Mathematics and Statistics, vol 241, pp.
395-408. Springer International Publishing.
Nguyen, N., Ökten, G. (2016). The acceptance-rejection
algorithm for low-discrepancy sequences. Monte Carlo Methods and
Applications, 22(2), 133-148.
Huang, W., Ewald, B., Ökten, G. (2016). CAM Stochastic Volatility
Model for Option Pricing. Mathematical Problems in
Engineering, Vol 2016,
(Special issue on
Nonlinear Problems: Mathematical Modeling, Analyzing,
and Computing for Finance 2016.)
Liu, Y., Hussaini, M. Y., & Ökten,
G. (2016). Accurate Construction of High Dimensional
Model Representation with Applications to Uncertainty
Quantification. Reliability Engineering &
System Safety, 152, 281-295.
Göncü, A., Liu, Y., Ökten, G., Hussaini, Y. (2016). Global
Sensitivity Analysis in Weather Derivatives Pricing. In Ronald
Cools, & Dirk Nuyens (Eds.), Monte Carlo and Quasi-Monte
Carlo Methods, MCQMC, Leuven, Belgium, April 2014 (pp. 15).
Springer Proceedings in Mathematics & Statistics Vol 163,
Springer-Verlag.
Liu, Y., Hussaini, M. Y., & Ökten, G. (2015). Global
Sensitivity Analysis for the Rothermel Model Based on High
Dimensional Model Representation. Canadian Journal of Forest
Research 45(11), 1474-1479.
(Earlier version appeared in the conference proceedings: In Wade
D.D & Fox R.L (Eds), Robinson ML (Comp), Proceedings of 4th
Fire Behavior and Fuels Conference, 18-22 February 2013,
Raleigh, NC and 1-4 July 2013, St. Petersburg, Russia (pp.
51-61). International Association of Wildland Fire: Missoula,
MT.)
Xu, L., & Ökten, G. (2015). High Performance Financial
Simulation Using Randomized Quasi-Monte Carlo Methods.
Quantitative Finance 15 (8), 1425-1436. doi:
10.1080/14697688.2015.1032549
Liu, Y., Jimenez, E., Hussaini, Y. M., & Ökten, G.,
Goodrick, S. (2015). Parametric Uncertainty Quantification in the
Rothermel Model with Randomized Quasi-Monte Carlo Methods.
International Journal of Wildland Fire, 24, 307-316.
http://dx.doi.org/10.1071/WF13097
Yuan, W., Göncü, A., & Ökten, G. (2015).
Estimating Sensitivities of Temperature Based Weather
Derivatives. Applied Economics, 47 (19), 1942-1955. doi: 10.1080/00036846.2014.1002888
Göncü, A., & Ökten, G. (2014). Efficient Simulation of a
Multi-factor Stochastic Volatility Model. Journal of Computational
and Applied Mathematics, 259, 329-335. doi:
10.1016/j.cam.2013.03.002
Göncü, A., & Ökten, G. (2014). Uniform point sets and the
collision test. Journal of Computational and Applied Mathematics,
259, 798-804. doi: 10.1016/j.cam.2013.07.019
Liu, Y., Hussaini, M. Y., & Ökten, G. (2013). Optimization of
a Monte Carlo Variance Reduction Method Based on Sensitivity
Derivatives. Applied Numerical Mathematics, 72, 160-171. doi:
10.1016/j.apnum.2013.06.005
Ökten, G., Shah, M., & Goncharov, Y. (2012). Random and
Deterministic Digit Permutations of the Halton Sequence. In Lezsek
Plaskota, & Henrik Woźniakowski (Eds.), 9th International
Conference on Monte Carlo and Quasi-Monte Carlo Methods in
Scientific Computing, Warsaw, Poland, August 15-20, 2010 (pp.
589-602). Springer-Verlag Berlin Heidelberg.
Ökten, G., & Göncü, A. (2011). Generating low-discrepancy
sequences from the normal distribution: Box-Muller or inverse
transformation? Mathematical and Computer Modelling, 53,
1268-1281.
Ökten, G., & Willyard, M. (2010). Parameterization based on
randomized quasi-Monte Carlo methods. Parallel Computing, 36,
415-422.
(This article also appeared in the Proceedings of the 1st Intl.
Workshop on Parallel and Distributed Computing in Finance, IEEE
International Parallel & Distributed Processing Symposium,
4/18/08, Miami, FL.)
Tiryakioglu, M., Ökten, G., Hudak, D., Shuey, R. T., & Suni,
J. P. (2010). On Evaluating Fit of the Lifshitz-Slyozov-Wagner
(LSW) Distribution to Particle Size Data. Materials Science &
Engineering A, 527, 1636-1639.
Ökten, G., & Gnewuch, M. (2009). Correction of a Proof in "A
Probabilistic Result on the Discrepancy of a Hybrid-Monte Carlo
Sequence and Applications". Monte Carlo Methods and Applications,
15, 2, 169-172.
Gisser, M., McClure, J., Ökten, G., & Santoni, G. (2009). Some
Anomalies Arising from Bandwagons that Impart Upward-Sloping
Segments to Market Demand. Econ Journal Watch, 6, 1, 21-34.
Tiryakioglu, M., Ökten, G., & Hudak, D. (2009). Statistics for
Estimating the Population Average of a Lifshitz-Slyozov-Wagner
(LSW) Distribution. Journal of Materials Science, 44, 21,
5754-5759.
Tiryakioglu, M., Ökten, G., & Hudak, D. (2009). On Evaluating
Weibull Fits to Mechanical Testing Data. Materials Science &
Engineering A, 527, 1-2, 397-399.
Õkten, G. (2009). Generalized von Neumann-Kakutani transformation
and random-start scrambled Halton sequences. Journal of
Complexity, 25, 4, 318-331.
Ökten, G., Salta, E., & Göncü, A. (2008). On Pricing Discrete
Barrier Options Using Conditional Expectation and Importance
Sampling Monte Carlo. Mathematical and Computer Modelling, 47,
484-494.
Goncharov, Y., Ökten, G., & Shah, M. (2007). Computation of
the endogenous mortgage rates with randomized quasi-Monte Carlo
simulations. Mathematical and Computer Modelling, 46, 459-481.
Lorch, J., & Ökten, G. (2007). Primes and Probability: The
Hawkins Random Sieve. Mathematics Magazine, 80, 2, 112-119.
Ökten, G., Tuffin, B., & Burago, V. (2006). A central limit
theorem and improved error bounds for a hybrid-Monte Carlo
sequence with applications in computational finance. Journal of
Complexity, 22, 4, 435-458.
Sivakumar, A., Bhat, C. R., & Ökten, G. (2006). Simulation
Estimation of Mixed Discrete Choice Models with the Use of
Randomized Quasi-Monte Carlo Sequences: A Comparative Study.
Transportation Research Record, 1921, 112-122.
Ökten, G. (2005). Solving Linear Equations by Monte Carlo Methods.
SIAM Journal on Scientific Computing, 27, 2, 511-531.
Ökten, G., & Eastman, W. (2004). Randomized Quasi-Monte Carlo
Methods in Pricing Securities. Journal of Economic Dynamics and
Control, 28, 2399-2426.
Ökten, G. (2002). Random Sampling from Low-Discrepancy Sequences:
Applications to Option Pricing. Mathematical and Computer
Modelling, 35, 1221-1234.
Ökten, G., & Srinivasan, A. (2002). Parallel Quasi-Monte Carlo
Applications on a Heterogeneous Cluster. In Kai T. Fang, Fred J.
Hickernell, & Harald Niederreiter (Eds.), Proceedings of the
Fourth International Conference on Monte Carlo and Quasi-Monte
Carlo Methods in Scientific Computing, Hong Kong Baptist
University, Hong Kong, China (pp. 406-421). Springer-Verlag,
Berlin, 2002.
Thomas, D. A., Ökten, G., & Buis, P. (2002). On-Line
Assessment of Higher-Order Thinking Skills: A Java-Based Extension
to Closed-Form Testing. In Sixth International Conference on
Teaching Statistics, Durban, South Africa (pp. 1-4). International
Association for Statistical Education, The University of Auckland,
New Zealand. Retrieved from
http://www.stat.auckland.ac.nz/~iase/publications/1/6d4_thom.pdf
Ökten, G. (2001). High Dimensional Simulation. Mathematics and
Computers in Simulation, 55, 215-222.
Ökten, G. (2000). Applications of a Hybrid-Monte Carlo Sequence to
Option Pricing. In Harald Niederreiter, & Jerome Spanier
(Eds.), Third International Conference on Monte Carlo and
Quasi-Monte Carlo Methods in Scientific Computing, Claremont
Graduate University, Claremont, CA (pp. 391-406). Springer-Verlag,
Berlin.
Ökten, G. (1999). Quasi-Monte Carlo Methods in Option Pricing.
Mathematica in Education and Research, 8, 3-4, 52-57.
Ökten, G. (1999). Error Reduction Techniques in Quasi-Monte Carlo
Integration. Mathematical and Computer Modelling, 30, 7-8, 61-69.
Ökten, G. (1998). Error Estimation for Quasi-Monte Carlo Methods.
In Harald Niederreiter, Peter Hellekalek, Gerhard Larcher, &
Peter Zinterhof (Eds.), Second International Conference on Monte
Carlo and Quasi-Monte Carlo Methods in Scientific Computing,
University of Salzburg, Austria (pp. 353-368). Springer-Verlag,
New York.
Ökten, G. (1996). A Probabilistic Result on the Discrepancy of a
Hybrid-Monte Carlo Sequence and Applications. Monte Carlo Methods
and Applications, 2, 4, 255-270.
Newsletter Articles
Ökten, G. (2000, November). Mathematics Explore
New Ideas, Uses. Claremont COURIER, 20.
Ökten, G. (1998, February). Monte Carlo Methods. Arctic Region
Supercomputing Center CRAY T3E Users' Group Newsletter, 136, N/A.