Publications

I am grateful for the support provided by various agencies through various grants, including NSF grants DMS-0595595, DMS-0806145 / 0902075, CAREER award CMMI-0846816, CMMI-1069064, DMS-1320550, CMMI-1436700, CMMI-1538217, CMMI-1538217, CMMI-1538217, DMS-1915967, DMS-1820942, DMS-1838676 as well as DARPA award N660011824028.

 

Publications and Scholarly Works

      Articles in Journals (published or accepted for publication)

      Articles in Journals (submitted)

      Conference Proceedings (published or accepted for publication)

      Book Chapters and Encyclopedia Articles

      Editorials

      Articles Submitted for Publication

      Sample Working Papers

 

1.        Doctoral thesis title

 

Limit Theorems and Approximations with Applications to Insurance Risk and Queueing Theory (2004) Stanford University, Department of Management Science and Engineering. Advisor, Professor Peter Glynn

 

2.        Articles in Journals (published or accepted for publication)

 

1.     Pekoz, E., and Blanchet, J.  Heavy-traffic Limit Theorems via Embeddings. Probability in the Engineering and Informational Sciences, 20 (2006), pp. 595-598

2.     Blanchet, J., and Glynn, P.  Corrected Diffusion Approximations for the Maximum of Light-tailed Random Walk. Annals of Applied Probability, 16 (2006), 2, pp. 952-983. (T)

3.     Blanchet, J., and Glynn, P. Uniform Renewal Theory with Applications to Geometric Sums. Advances in Applied Probability, 39 (2007), 4, pp 1070 – 1097.

4.     Blanchet, J., Glynn, P., and Liu, J. C. Fluid Heuristics, Lyapunov Bounds and Efficient Importance Sampling for a Heavy-tailed G/G/1 Queue. Queueing Systems: Theory and Applications, 56 (2007), 3, pp. 99 – 113. (S) INFORMS Applied Probability Society Best Publication Award.

5.     Blanchet, J. and Glynn, P. Efficient Rare Event Simulation for the Single Server Queue with Heavy Tailed Distributions.  Annals of Applied Probability, 18 (2008), 4, pp. 1351 – 1378. INFORMS Applied Probability Society Best Publication Award.

6.     Blanchet, J., and Liu, J. C. State-dependent Importance Sampling for Regularly Varying Random Walks. Advances in Applied Probability, 40, (2008), pp 1104-1128. (S)

7.     Asmussen, S., Blanchet, J., Rojas-Nandayapa, L., and Juneja, S. Efficient Simulation of Tails Probabilities of Sums of Correlated Lognormals. To appear in Annals of Operations Research, Special vol. in honor of Reuven Rubinstein.

8.     Blanchet, J., Glynn, P., and Lam, H. Rare-event Simulation of a Slotted Time M/G/s Queue. Queueing Systems: Theory and Applications, 67, (2009), pp 33 – 57. (S), (I)

9.     Olvera-Cravioto, M., Blanchet, J. and Glynn P. On the Transition from Heavy Traffic to Heavy Tails for the M/G/1 Queue I:  The Regularly Varying Case. Annals of Applied Probability, 21, (2011), pp 645-668. (C)

10.  Blanchet, J. Importance Sampling and Efficient Counting for Binary Contingency Tables. Annals of Applied Probability, 19, (2009),  pp 949 – 982. INFORMS Applied Probability Society Best Publication Award.

11.  Blanchet, J., and Li, C. Efficient Rare-event Simulation for Heavy-tailed Compound Sums. ACM TOMACS Transactions in Modeling and Computer Simulation, 21, (2011), pp 1-10.

12.  Blanchet, J., and Li, C. Efficient Simulation for the Maximum of Infinite Horizon Gaussian Processes. To appear in Journal of Applied Probability. (S)

13.  Blanchet, J. and Liu, J. Efficient Importance Sampling in Ruin Problems for Multidimensional Regularly Varying Random Walks. Journal of Applied Probability, 47, (2010), 301-322.* (S) INFORMS Junior Faculty Interest Group Forum Competition (Finalist).

14.  Blanchet, J. and Zwart, B. Asymptotic Expansions of Renewal Equations with Applications to Insurance and Processor Sharing. Math. Meth. in Oper. Res., 72, (2010), 311-326.

15.  L'Ecuyer, P., Blanchet, J., Tuffin, B., and Glynn, P. W.  Asymptotic Robustness of Estimators in Rare-Event Simulation. ACM TOMACS Transactions in Modeling and Computer Simulation, 20, (2010), pp 1-41.

16.  Lam, H. K., Blanchet, J., Bazant, M., and Burch, D. Corrections to the Central Limit Theorem for Heavy-tailed Probability Densities. To appear in Journal of Theoretical Probability. (Available through on-line first since September 17, 2011.)  (S)

17.  Blanchet, J., Leder, K., Shi, Y. Analysis of a Splitting Estimator for Rare Event Probabilities in Jackson Networks. Stochastic Systems, 1, (2011), pp 306-339. (P, S)

18.  Blanchet, J., and Rojas-Nandayapa, L. Efficient Simulation of Tail Probabilities of Sums of Dependent Random Variables. Journal of Applied Probability, Special Vol. 48A, (2011), 147-165.

19.  Blanchet, J., and Sigman, K. On Exact Sampling of Stochastic Perpetuities. Journal of Applied Probability, Special Vol. 48A, (2011), 165-183. (C)

20.  Blanchet, J., and Lam, H. State-dependent Importance Sampling for Rare Event Simulation: An Overview and Recent advances. Surveys in Operations Research and Management Sciences, 17, (2012), 38-59 (S)

21.  Adler, R., Blanchet, J., and Liu, J. C. Efficient Simulation of High Excursions of Gaussian Random Fields. Annals of Applied Probability, 22, (2012), 1167-1214. (S)

22.  Blanchet, J. and Pacheco-Gonzales, C. Uniform Convergence to a Law Containing Gaussian and Cauchy Distributions. Probability in the Engineering and Informational Sciences, 26, (2012), 437-448

23.  Blanchet, J., and Stauffer, A. Characterizing Optimal Sampling of Binary Contingency Tables via the Configuration Model. Random Structures and Algorithms, 42, 159-184 (2012). (See also http://arxiv.org/abs/1007.1214.)

24.  Blanchet, J., Glynn, P., and Leder, K. On Lyapunov Inequalities and Subsolutions for Efficient Importance Sampling. ACM TOMACS Transactions in Modeling and Computer Simulation, 22, (2012). Article No. 13.

25.  Blanchet, J., Lam, H., and Zwart, B. Efficient Rare Event Simulation for Perpetuities. Stochastic Processes and their Applications, 122, (2012), 3361-3392. (S)

26.  Blanchet, J. Optimal Sampling of Overflow Paths in Jackson Networks.  Mathematics of Operations Research, 38, (2013), 698-719.

27.  Blanchet, J., and Liu, J. C. Efficient Simulation and Conditional Functional Limit Theorems for Ruinous Heavy-tailed Random Walks. Stochastic Processes and their Applications, 122, (2012), 2994-3031. (S)

28.  Blanchet, J. and Shi, Y. Strongly Efficient Algorithms via Cross Entropy for Heavy- tailed Systems. Operations Research Letters, 41, (2013), 271-276. (S)

29.  Blanchet, J., and Liu, J. C. Total Variation Approximations for Multivariate Regularly Varying Random Walks Conditioned on Ruin. Bernoulli, 20, (2014), 416-456. (S)

30.  Blanchet, J., Glynn, P., and Meyn, S. Large Deviations for the Empirical Mean of an M/M/1 Queue. Queueing Systems: Theory and Applications, 73, (2013), 425-446.

31.  Blanchet, J. and Lam, H. A Heavy Traffic Approach to Modeling Large Life Insurance Portfolios. Insurance: Mathematics and Economics, 53, (2013), 237-251. (S)

32.  Blanchet, J. and Mandjes, M. Asymptotics of the Area under the Graph of a Lévy-driven Workload Process. Operations Research Letters, 41, (2013), 730-736.

33.  Blanchet, J., Hult, H., and Leder, K. Rare-event simulation for stochastic recurrence equations with heavy-tailed innovations. ACM TOMACS Transactions on Modeling and Computer Simulations. (Supplement), 23, (2013), Article No. 22.

34.  Blanchet, J., and Lam, H. Rare-event Simulation for Many Server Queues. Mathematics of Operations Research, 39, (2014), 1142–1178. (S) Honorable mention in the 2011 INFORMS Nicholson Student Paper Competition (as supervisor).   

35.  Blanchet, J., Chen, X., and Lam, H. Two-parameter Sample Path Large Deviations for Infinite Server Queues. Stochastic Systems, 4, (2014), 206-249.

36.  Blanchet, J. and Lam, H. Uniform Large Deviations for Heavy-Tailed Queues under Heavy-Traffic. Bulletin of the Mexican Mathematical Society, Bol. Soc. Mat. Mexicana (3) Vol. 19, 2013 Special Issue for the International Year of Statistics.

37.  Blanchet, J. and Chen, X. Steady-state Simulation for Reflected Brownian Motion and Related Networks. To appear Annals of Applied Probability.

38.  Murthy, K., Juneja, S., and Blanchet, J. State-independent Importance Sampling for Random Walks with Regularly Varying Increments. To appear Stochastic Systems.

39.  Blanchet, J. and Dong, J. Perfect Sampling for Infinite Server and Loss Systems. Advances in Applied Probability, 47, (2015).

40.  Blanchet, J. and Wallwater, A. Exact Sampling for the Steady-state Waiting Time of a Heavy-tailed Single Server Queue. ACM TOMACS Transactions on Modeling and Computer Simulations, 25 (4) (2015) http://dl.acm.org/citation.cfm?id=2822892&CFID=594568455&CFTOKEN=74958442

41.  Blanchet, J., Gallego, G. and Goyal, V. A Markov Chain Approximation to Choice Modeling. Operations Research, Vol. 64-4, pp. 771-1051, 2016. Best Operations Research Paper, INFORMS MSOM Society.

42.  Zhang, X., Blanchet, J., Giesecke, K., and Glynn, P. Affine Point Processes: Approximation and Efficient Simulation. Mathematics of Operations Research, Vol. 40, (2015), pp.797-819.

43.  Blanchet, J., and Murthy, K. Tail Asymptotics for Large Delays in a Half-Loaded GI/GI/2 Queue with Heavy-Tailed Job Sizes. Queueing Systems: Theory and Applications, 81, (2015), 301-340.

44.  Bienstock, D., Li, J., and Blanchet, J. Stochastic Models and Control for Electrical Power Line Temperature. Energy Systems, 7, (2016), 173-192.

45.  Blanchet, J. and Ruf, J. A Weak Convergence Criterion Constructing Changes of Measure. Stochastic Models, 22, (2016). http://www.tandfonline.com/doi/abs/10.1080/15326349.2015.1114891?journalCode=lstm20

46.  Blanchet, J., Glynn, P., and Zheng, J. Theoretical Analysis of a Stochastic Approximation Approach for Computing Quasi-stationary Distributions. Advances in Applied Probability, 48, (2016) 792-811.

47.  Blanchet, J., Chen, X., and Dong, J.  ε-Strong Simulation of Multidimensional Stochastic Differential Equations via Rough Path Analysis. Annals of Applied Probability, 27, (2017), 275-339. http://arxiv.org/abs/1403.5722

48.  Blanchet, J. and Murthy, K. Exact Simulation of Multidimensional Reflected Brownian Motion. Journal of Applied Probability, Vol. 55, (2018), pp. 137-159.

49.  Blanchet, J., Dong, J., and Pei, Y. Perfect Sampling of G/G/c Queues. Queueing Systems: Theory and Applications, 90, (2018) pp. 1-33.

50.  Blanchet, J., Pei, Y., Sigman, K. Exact sampling for some multi-dimensional queueing models with renewal input. Advances in Applied Probability, 51, 4, (2019), 1179-1208.

51.  Liu, Z., Blanchet, J., Dieker, T., and Mikosch, T. On logarithmically optimal exact simulation of max-stable and related random fields on a compact set. Bernoulli, 25-4A, (2019), pp. 2949-298.

52.  Blanchet, J., Dong, J., and Liu, Z. Exact Sampling of the Infinite Horizon Maximum of a Random Walk over a Non-linear Boundary. Journal of Applied Probability, 56, 1, (2019), pp. 116-138.

53.  Blanchet, J., Li, J., and Nakayama, M. Efficient Monte Carlo Methods for Estimating Failure Probabilities of a Distribution Network with Random Demands. Operations Research, 19, (2019), pp. 3837-3848.

54.  Rhee, C-H., Blanchet, J., and Zwart, B. Sample Path Large Deviations for Heavy-Tailed Lévy Processes and Random Walks. Annals of Probability, 17, (2019), pp. 3551-3605.

55.  Bohan, Blanchet, J., Rhee, C-H., and Zwart, B. Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Random Walks and Compound Poisson Processes. Mathematics of Operations Research, 44-3, (2019), pp. 919-942.

56.  Blanchet, J., and Chen, X., Perfect Sampling of Generalized Jackson Networks. Mathematics of Operations Research., 44-2, (2019), pp. 693-714.

57.  Blanchet, J., Lam, H.,  Tang, Q., and Yuan, Z. Applied Robust Performance Analysis for Actuarial Applications. North American Actuarial Journal, 23, (2019), 33-63.

58.  Blanchet, J. and Murthy, K. Quantifying Distributional Model Risk via Optimal Transport. Mathematics of Operations Research, 44-2, (2019), pp. 377-766.

59.  Blanchet, J., Kang, Y., Murthy, K. Robust Wasserstein Profile Inference and Applications to Machine Learning. Journal of Applied Probability, 56, (2019), pp. 830-857.

60.  Blanchet, J., Cartis, C., Menickelly, M., and Scheinberg, K. Analysis of a Stochastic Trust Region Method via Supermartingales. INFORMS Journal on Optimization. 1(2), (2019), pp. 92-119.

61.  Blanchet, J. and Si, N. “Optimal uncertainty size in distributionally robust inverse covariance estimation,” Operations Research Letters, Vol. 47, 6, (2019), pp. 618-621.

62.  Blanchet, J., He, F., Murthy, K., On Distributionally Robust Extreme Value Theory. To appear in Extremes.

63.  Blanchet, J. and Kang, Y. Sample-out-of-sample Inference Based on Wasserstein Distance. To appear in Operations Research.

64.  Blanchet, J., and Chen, X., Rates of Convergence to Stationarity for Multidimensional RBM. To appear in Mathematics of Operations Research.

 

 

 

 

3.        Articles submitted or under review

 

65.  Blanchet, J., Chen, X., Glynn, P., Si, N. Efficient Steady-state Simulation of High-dimensional Stochastic Networks

66.  Blanchet, J., Zhang, F., Zwart, B. Optimal Scenario Generation for Heavy-tailed Chance Constrained Optimization

67.  Blanchet, J., Murthy, K., Zhang, F. Optimal Transport Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes

68.  Blanchet, J., Murthy, K., Si, N. Confidence Regions in Wasserstein Distributionally Robust Estimation.

69.  Blanchet, J., Reiman, M., Shah, V., and Wein, L. Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities

70.  Blanchet, J., Glynn, P., Pei, Y. Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state Simulation, Quantiles, and Other Applications

71.  Mahdian, S., Blanchet, J., Glynn, P. Optimal Transport Relaxations with Application to Wasserstein GANs

72.  Blanchet, J., Jambulapati, A., Kent, C., Sidford, A. Towards Optimal Running Times for Optimal Transport

73.  Blanchet, J., and Zhang, F. Exact Simulation for Multivariate Ito Diffusions

74.  Blanchet, J., and Chen, X., Pei, Y. Unraveling Limit Order Books Using Just Bid/Ask Prices

75.  Blanchet, J., and Kang, Y. Semi-supervised Learning based on Distributionally Robust Optimization (also posted in ArXiv as Distributionally Robust Semi-supervised Learning)

76.  Blanchet, J., Kang, Y., Zhang, F., and Murthy, K. Data-driven Optimal Transport Cost Selection for Distributionally Robust Optimization

77.  Blanchet, J., Kang, Y., Zhang, F., He, F., and Hu, Z. Doubly Robust Data-Driven Distributionally Robust Optimization

78.  Blanchet, J., Goldfarb, D., Iyengar, G., Li, F., Zhou, C. Unbiased Simulation for Optimizing Stochastic Function Compositions.

 

4.        Conference Proceedings (published or accepted for publication)

Note:  + represents a paper for which there is journal version listed above, otherwise there is no overlap in content with papers that have appeared in journals.

79.  Blanchet, J., and Glynn, P. Strongly-efficient Estimators for Light-tailed Sums. ACM: Proc Valuetools’06, Article 18,  (2006). (S)

80.  Blanchet, J., Liu, J. C. and Glynn, P. Importance Sampling and Large Deviations. Proc. Valuetools’06, Article 20,  (2006). (S)

81.  Blanchet, J., and Liu, J. C. Efficient Simulation of Large Deviation Probabilities for Sums of Heavy-tailed Increments. Proc. Winter Simulation Conference (2006), pp. 757-764. (S) +

82.  Blanchet, J., and Zwart, B.  Importance Sampling of Compounding Processes. Proc. Winter Simulation Conference (2007), pp. 372-379. (I) +

83.  Blanchet, J., and Liu, J. C.  Rare-event Simulation of Multidimensional Random Walks with t-distributed Increments. Proc. Winter Simulation Conference (2007), pp. 395-402. (S) (I) +

84.  Blanchet, J., and Liu, J. C. Path-sampling for State-dependent Importance Sampling. Proc. Winter Simulation Conference (2007), pp. 380-388.  (S) (I)

85.  Zhang, X., Blanchet, J., and Glynn, P. Efficient Suboptimal Rare-event Simulation. Proc. Winter Simulation Conference (2007), pp. 389-394. (I)

86.  Blanchet, J., Rojas-Nandayapa, L., and Juneja, S. Fast Simulation of Sums of Correlated Lognormals. Proc. Winter Simulation Conference (2008), pp. 607-614. +

87.  Adler, R., Blanchet, J. and Liu, J. C. Efficient Simulation for Tail Probabilities of Gaussian Random Fields. Proc. Winter Simulation Conference (2008), pp 328-336. (S) (I) +

88.  Blanchet, J., Liu, J. C., and Zwart, B. A Large Deviations Perspective to Ordinal Optimization of Heavy-tailed Systems. Proc. Winter Simulation Conference (2008), pp. 489-494. (S) (I)

89.  Blanchet, J., Leder, K. and Glynn, P. Efficient Simulation for Light-tailed Sums: An Old Folk Song Sung to a Faster New Tune. Springer volume for MCQMC 2008 edited by Pierre L’Ecuyer and Art Owen. (2009), pp. 227-248. (P)

90.  Blanchet, J., and Glynn, P. Efficient Rare Event Simulation of Continuous Time Markovian Perpetuities. Proc. Of the Winter Simulation Conference (2009), pp. 444-451. (I)

91.  Zhang, X., Glynn, P., Giesecke, K., Blanchet, J. Rare Event Simulation of a Generalized Hawkes Process. Proc. Of the Winter Simulation Conference (2009), pp. 1291-1298. (I)

92.  Blanchet, J., Liu, J. C., and Xang, X. Monte Carlo for Large Credit Portfolios with Potentially High Correlations. Proc. of the Winter Simulation Conference (2010), pp. 328-336. (S) (I)

93.  Blanchet, J., and Lam, H. Rare Event Simulation Techniques. Proc.  Winter Simulation Conference (2011). (S) (I)

94.  Blanchet, J., and Shi, Y. Strongly Efficient Cross Entropy Method for Heavy-tailed Simulation, Winter Simulation Conference (2011). (S) (I)

95.  Blanchet, J., Li, Juan, and Nakayama, M. A Conditional Monte Carlo for Estimating the Failure Probability of a Network with Random Demands (2011). (S) (I)

96.  Blanchet, J., Hult, H., and Leder, K. Efficient Importance Sampling for Affine Regularly Varying Markov Chains (2011).

97.  Blanchet, J., and Lam, H. Importance Sampling for Actuarial Cost Analysis under a Heavy Traffic Model. Proc.  Winter Simulation Conference (2011). (S) (I)

98.  Blanchet, J., and Dong, J. Sampling point processes on stable unbounded regions and exact simulation of queues. Proc. Winter Simulation Conference (2012): 11 (S) (I)

99.  Blanchet, J., Glynn, P., and Zheng, S. Empirical Analysis of a Stochastic Approximation Approach for Computing Quasi-stationary Distributions. EVOLVE 2012: 19-37

100.         Blanchet, J., Gallego, G., and Goyal, G. A Markov chain approximation to choice modeling. ACM Conference on Electronic Commerce 2013: 103-104

101.         Blanchet, J. and Shi, Y. Efficient Rare Event Simulation via Particle Methods for Heavy-tailed Sums. Proc. Winter Simulation Conference (2013), pp. 724-735.

102.         Blanchet, J., Murthy, K., and Juneja, S. Optimal Rare Event Monte Carlo for Markov Modulated Regularly Varying Random Walks. Proc. Winter Simulation Conference (2013), 564-576.

103.         Bienstock, D., Blanchet, J., and Li, J. Stochastic Models and Control for Electrical Power Line Temperature. Proc. 51st Annual Allerton Conference on Communication, Control, and Computing (2013), 1344-1348.

104.         Blanchet, J., Dolan, C., and Lam, H. Robust Rare-Event Performance Analysis with Natural Non-Convex Constraints. Proc. Winter Simulation Conference (2014), pp. 595-603.

105.         Shanbhag, U., and Blanchet, J. Budget-Constrained Stochastic Approximation. Proc. Winter Simulation Conference (2015), 368-379.

106.         Blanchet, J., Chen, N., and Glynn, J. Unbiased Monte Carlo Computation of Smooth Functions of Expectations via Taylor Expansions. Proc. Winter Simulation Conference (2015), 360-367.

107.         Blanchet, J., and Glynn, J. Unbiased Monte Carlo for Optimization and Functions of Expectations via Multilevel Randomization. Proc. Winter Simulation Conference (2015), 3656-3667. 

108.         Blanchet, J., He, F., and Lam, H. Computing Worst Case Expectations Given Marginals via Simulation. Proc. Winter Simulation Conference (2017), to appear.

109.         Blanchet, J., and Kang, Y. Distributionally Robust Groupwise Regularization Estimator (2017), to appear.

110.         Blanchet, J. and Liu, Z. Malliavin-based Multilevel Monte Carlo Estimators for Densities of Max-stable Processes. Springer Proceedings in Mathematics & Statistics, (2016) pp. 75-97.

111.         Blanchet, J. and Kang, Y. “Semi-supervised learning based on distributionally robust optimization,” Proceedings of 5th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop, 2018.

112.         Shah, V., Blanchet, J., and Johari, R. “Bandit learning with positive externalities,” Advances in Neural Information Processing Systems (NeurIPS), 31, 2018.

113.         Chu, C., Blanchet, J., and Glynn, P. “Probability functional descent: A unifying perspective on GANs, variational inference, and reinforcement learning,” ICML 2019: 1213-1222.

114.         Shah, V., Blanchet, J., and Johari, R. “Semi-parametric dynamic contextual pricing,” Advances in Neural Information Processing Systems (NeurIPS), 32, 2019.

115.         Blanchet, J., Glynn, P., Yan, J., and Zhou, Z. “Multivariate distributionally robust convex regression under absolute error loss,” Advances in Neural Information Processing Systems (NeurIPS), 32, 2019.

116.         Zhou, Z., Xu, R., and Blanchet J., “Learning in generalized linear contextual bandits with stochastic delays,” Advances in Neural Information Processing Systems (NeurIPS), 32, 2019.

117.         Bistritz, I., Zhou, Z., Chen, X., Bambos, N., and Blanchet, J. “EXP3 Learning in adversarial bandits with delayed feedback,” Advances in Neural Information Processing Systems (NeurIPS), 32, 2019.

118.         Blanchet, J., Kang, Y., Murthy, K., and Zhang, F. “Data-driven optimal transport cost selection for distributionally robust optimization,” Proceedings of the 2019 Winter Simulation Conference, 2019, (Best Theoretical Paper Award)

119.         Blanchet, J., Kang, F., Zhang, F., and Zhangyi Hu, Z. “A distributionally robust boosting algorithm,” Proc. Winter Simulation Conference, 2019. N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y.-J. Son, eds.

 

5.        Chapters in Books (published or accepted for publication)

120.         Blanchet, J., and Rudoy, D. Rare-event Simulation and Counting Problems. In Rare-event Estimation using Monte Carlo Methods, Rubino, G. and Tuffin, B. Eds. Wiley, 2009.

121.         Blanchet, J., and Mandjes, M. Rare-event Simulation for Queues. In Rare-event Estimation using Monte Carlo Methods, Rubino, G. and Tuffin, B. Eds. Wiley, 2009.

122.         Blanchet, J. and Pacheco-Gonzales C. Large Deviations and Applications to Quantitative Finance. Encyclopedia of Quantitative Finance, Edited by Rama Cont. Wiley 2009.

 

6.        Editorials

123.         Blanchet, J., and Mandjes, M. Rare-event Simulation for Queues (2007). Queueing Systems: Theory and Applications. Vol. 57 Numbers 2 and 3. Editorial.

124.         Blanchet, J., and Roberts, G. Simulation of Stochastic Networks and related topics (2012). Queueing Systems: Theory and Applications. Vo. 73 Numbers 4. Editorial.

 

7.        Some Preprints and Technical Reports. BEWARE the presentation requires polishing, but the math should be fine – however, I’d appreciate comments if you see any problem. Also, please, email me if you’re interested in any of the preprints below and it is not uploaded.

125.         Blanchet, J., Liu, J. C., and Glynn, P. Efficient Rare Event Simulation for Regularly Varying Multi-server Queues. To be submitted to Queueing Systems Theory and Applications. Summary: This paper provides the first asymptotically optimal (in fact we show strong optimality) algorithm for estimating the tails of the steady-state delay in a multi-server queue with heavy-tailed increments. The technique is the one introduced in “Fluid Heuristics, Lyapunov Bounds and Efficient Importance Sampling for a Heavy-tailed G/G/1 Queue (with P. Glynn, and J. C. Liu), 2007. QUESTA, 57, 99-113”. The construction in the multidimensional case is interesting because of the way in which fluid heuristics need to adapt to accommodate the boundaries.

126.         Blanchet, J. and Glynn, P. Large Deviations and Sharp Asymptotics for Perpetuities with Small Discount Rates . Summary: This paper relates to Approximations for the Distribution of Perpetuities with Small Discount Rates (with P. Glynn). Instead of concentrating on the central limit theorem region we develop large deviations asymptotics. The paper contains characterizations of exponential tightness in a suitable (and useful for the analysis of perpetuities) class of topologies. It also develops exact tail asymptotics for discrete and continuous perpetuities and it shows qualitative differences arising from the discrete and continuous nature of perpetuities. I think this line of research is particularly interesting these days in which the interest rates are small.

127.         Blanchet, J. and Glynn, P. Corrected Diffusion Approximations for the Maximum of Random Walks with Heavy-tails (with P. Glynn). (Please, refer to Chapter 3 of my dissertation for more details). Summary: This paper continues the study of corrected diffusion approximations for first passage times of random walks with a small negative drift $mu$. The paper “Complete Corrected Diffusion Approximations for the Maximum of Random Walk (2006) Ann. of App. Prob., (with P. Glynn)” assumes finite exponential moments. Here we show that if one has $alpha + 2$ moments then one can add $alpha$ correction terms resulting in an approximation with an error of size o(mu^alpha).

128.         Blanchet, J., and Lam, H. K. Corrected Diffusion Approximations for Moments of the Steady-state Waiting Time in a G/G/1 Queue. Summary: This paper revisits Complete Corrected Diffusion Approximations for the Maximum of Random Walk (with P. Glynn), 2006. Ann. of App. Prob., 16, p. 951-953. We now concentrate on moments rather than the tail of the distribution. Recently Janssen and van Leeuwaarden (2007), Stoch. Proc. and their Appl., 117, 1928-1959, obtained complete asymptotic expansions for the Gaussian case. Here we obtain expansions for any strongly non-lattice distribution exponentially decaying tails.

129.         Blanchet, J., and Lam, H. K. Rare-event Simulation for Markov Modulated Heavy-tailed Random Walks. Summary: This paper considers rare-event simulation for first passage time probabilities of Markov modulated regularly varying random walks. We use the Lyapunov-bound technique to design the importance sampling estimator. What is interesting is that the Lyapunov bound, instead of being tested in one step of the underlying process, as we typically do, it must be tested after K steps (for K large enough) or at regeneration times of the underlying Markov modulation.

130.         Blanchet, J., and Meng, X. L. Exact Sampling, Regeneration and Minorization Conditions. Summary: This report builds on a paper by Asmussen, Glynn and Thorisson (1992) TOMACS. Here we note that if one has a Harris chain with regeneration time “tau” and if one can compute a constant C>0 such that E(tau^p)<=C for p>1 (or Eexp(delta*tau)<C) then one can generate exact samples from  the steady-state distribution of chain in question in finite time almost surely, provided that one can identify the regeneration times of the chain. Unfortunately, although the expected termination time will typically be infinite. However, I recently figured out how to fix the problem for a large class of chains, I hope to report on that in the not-so-distant future.

131.         Blanchet, J. and Shi, Y. Modeling and Efficient Rare Event Simulation of Systemic Risk in Insurance-Reinsurance Networks.

132.         Blanchet, J., and Glynn, P.  Approximations for the Distribution of Perpetuities with Small Interest Rates.

133.         Blanchet, J. and Dupuis, P. Fast Simulation of Brownian Motion Avoiding Random Obstacles.