Papers

Journal and conference publications, followed by newsletter articles.

Papers

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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
  6. Chen, Y., Ökten, G. (2022) A goodness-of-fit test for copulas based on the collision test. Statistical Papers, 63(5), 1369-1385.
  7. Salehy, N., Ökten, G. (2021) Dempster-Shafer Theory for Stock Selection. IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), July 12-16, 2021.
  8. Fox, J., Ökten, G. (2021) Polynomial Chaos as a Control Variate Method. SIAM Journal on Scientific Computing, 43(3), pp A2268-A2294.
  9. Fox, J., Ökten, G. (2021) Brownian Path Generation with Polynomial Chaos, SIAM Journal on Financial Mathematics, 12(2), pp 724-743.
  10. Ökten, G., Liu, Y. (2021) Randomized quasi-Monte Carlo methods in global sensitivity analysis. Reliability Engineering & System Safety, Volume 210, 107520, ISSN 0951-8320.
  11. Polala, A., Ökten, G. (2020) Implementing de-biased estimators using mixed sequences. Monte Carlo Methods and Applications, 26(4), 293-301.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. Nguyen, N., Ökten, G. (2016) The acceptance-rejection algorithm for low-discrepancy sequences. Monte Carlo Methods and Applications, 22(2), 133-148.
  19. 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.)
  20. 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.
  21. 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. Springer Proceedings in Mathematics & Statistics, Vol 163, Springer-Verlag.
  22. 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: 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.)
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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.
  30. 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.
  31. Ökten, G., & Willyard, M. (2010) Parameterization based on randomized quasi-Monte Carlo methods. Parallel Computing, 36, 415-422. (Also appeared in: 1st Intl. Workshop on Parallel and Distributed Computing in Finance, IEEE IPDPS, 4/18/08, Miami, FL.)
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. Tiryakioglu, M., Ökten, G., & Hudak, D. (2009) On Evaluating Weibull Fits to Mechanical Testing Data. Materials Science & Engineering A, 527(1-2), 397-399.
  37. Õkten, G. (2009) Generalized von Neumann-Kakutani transformation and random-start scrambled Halton sequences. Journal of Complexity, 25(4), 318-331.
  38. 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.
  39. 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.
  40. Lorch, J., & Ökten, G. (2007) Primes and Probability: The Hawkins Random Sieve. Mathematics Magazine, 80(2), 112-119.
  41. 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.
  42. 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.
  43. Ökten, G. (2005) Solving Linear Equations by Monte Carlo Methods. SIAM Journal on Scientific Computing, 27(2), 511-531.
  44. Ökten, G., & Eastman, W. (2004) Randomized Quasi-Monte Carlo Methods in Pricing Securities. Journal of Economic Dynamics and Control, 28, 2399-2426.
  45. Ökten, G. (2002) Random Sampling from Low-Discrepancy Sequences: Applications to Option Pricing. Mathematical and Computer Modelling, 35, 1221-1234.
  46. 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.
  47. 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
  48. Ökten, G. (2001) High Dimensional Simulation. Mathematics and Computers in Simulation, 55, 215-222.
  49. 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.
  50. Ökten, G. (1999) Quasi-Monte Carlo Methods in Option Pricing. Mathematica in Education and Research, 8(3-4), 52-57.
  51. Ökten, G. (1999) Error Reduction Techniques in Quasi-Monte Carlo Integration. Mathematical and Computer Modelling, 30(7-8), 61-69.
  52. 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.
  53. 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

  1. Ökten, G. (2000, November). Mathematics Explore New Ideas, Uses. Claremont COURIER, 20.
  2. Ökten, G. (1998, February). Monte Carlo Methods. Arctic Region Supercomputing Center CRAY T3E Users' Group Newsletter, 136, N/A.