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Marvin K. Nakayama
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- affiliation: New Jersey Institute of Technology, Newark, USA
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2020 – today
- 2024
- [j27]Yajuan Li
, Zachary T. Kaplan
Monte Carlo Methods for Economic Capital. INFORMS J. Comput. 36(1): 266-284 (2024) - [j26]Russell R. Barton
Introduction to the Special Issue for INFORMS Simulation Society (I-Sim) Workshop, 2021. ACM Trans. Model. Comput. Simul. 34(2): 5:1-5:3 (2024) - [j25]Marvin K. Nakayama
Sufficient Conditions for Central Limit Theorems and Confidence Intervals for Randomized Quasi-Monte Carlo Methods. ACM Trans. Model. Comput. Simul. 34(3): 13:1-13:38 (2024) - 2023
- [c47]Marvin K. Nakayama, Bruno Tuffin:
Efficiency of Estimating Functions of Means in Rare-Event Contexts. WSC 2023: 351-362 - [c46]Pierre L'Ecuyer, Marvin K. Nakayama, Art B. Owen, Bruno Tuffin:
Confidence Intervals for Randomized Quasi-Monte Carlo Estimators. WSC 2023: 445-456 - 2022
- [c45]Marvin K. Nakayama, Bruno Tuffin:
Density Estimators of the Cumulative Reward Up to a Hitting Time to a Rarely Visited Set of a Regenerative System. WSC 2022: 121-132 - 2021
- [c44]Marvin K. Nakayama, Bruno Tuffin:
Sufficient Conditions for a Central Limit Theorem to Assess the Error of Randomized Quasi-Monte Carlo Methods. WSC 2021: 1-12 - 2020
- [c43]Marvin K. Nakayama, Zachary T. Kaplan
Quantile Estimation Via a Combination of Conditional Monte Carlo and Randomized Quasi-Monte Carlo. WSC 2020: 301-312 - [c42]Peter W. Glynn, Marvin K. Nakayama, Bruno Tuffin:
Comparing Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set. WSC 2020: 421-432
2010 – 2019
- 2019
- [j24]Jose H. Blanchet
Rare-Event Simulation for Distribution Networks. Oper. Res. 67(5): 1383-1396 (2019) - [c41]Marvin K. Nakayama, Bruno Tuffin:
Efficient Estimation of the Mean Hitting Time to a set of A Regenerative System. WSC 2019: 416-427 - [c40]Zachary T. Kaplan
Randomized Quasi-Monte Carlo for Quantile Estimation. WSC 2019: 428-439 - 2018
- [c39]Zachary T. Kaplan
Monte Carlo estimation of Economic Capital. WSC 2018: 1754-1765 - [c38]Peter W. Glynn, Marvin K. Nakayama, Bruno Tuffin:
Using Regenerative simulation to Calibrate exponential Approximations to Risk Measures of Hitting times to rarely Visited Sets. WSC 2018: 1802-1813 - 2017
- [j23]Hui Dong
Quantile Estimation with Latin Hypercube Sampling. Oper. Res. 65(6): 1678-1695 (2017) - [j22]Andres Alban
Efficient Monte Carlo methods for estimating failure probabilities. Reliab. Eng. Syst. Saf. 165: 376-394 (2017) - [j21]Mihir Sanghavi, Sashank Tadepalli, Timothy J. Boyle, Matthew Downey, Marvin K. Nakayama:
Efficient Algorithms for Analyzing Cascading Failures in a Markovian Dependability Model. IEEE Trans. Reliab. 66(2): 258-280 (2017) - [c37]Russell R. Barton
History of improving statistical efficiency. WSC 2017: 158-180 - [c36]Peter W. Glynn, Marvin K. Nakayama, Bruno Tuffin:
On the estimation of the mean time to failure by simulation. WSC 2017: 1844-1855 - [c35]Hui Dong, Marvin K. Nakayama:
Quantile estimation using conditional Monte Carlo and Latin hypercube sampling. WSC 2017: 1986-1997 - 2016
- [j20]Dave Grabaskas, Marvin K. Nakayama, Richard Denning, Tunc Aldemir:
Advantages of variance reduction techniques in establishing confidence intervals for quantiles. Reliab. Eng. Syst. Saf. 149: 187-203 (2016) - [c34]Andres Alban
Variance reduction for estimating a failure probability with multiple criteria. WSC 2016: 302-313 - 2015
- [j19]James M. Calvin
Resampled Regenerative Estimators. ACM Trans. Model. Comput. Simul. 25(4): 23:1-23:25 (2015) - [c33]Marvin K. Nakayama:
Estimating a failure probability using a combination of variance-reduction techniques. WSC 2015: 621-632 - 2014
- [j18]Marvin K. Nakayama:
Confidence Intervals for Quantiles Using Sectioning When Applying Variance-Reduction Techniques. ACM Trans. Model. Comput. Simul. 24(4): 19:1-19:21 (2014) - [c32]Marvin K. Nakayama:
Quantile estimation when applying conditional Monte Carlo. SIMULTECH 2014: 280-285 - [c31]Marvin K. Nakayama:
Using sectioning to construct confidence intervals for quantiles when applying antithetic variates. SummerSim 2014: 24 - [c30]Hui Dong, Marvin K. Nakayama:
Constructing confidence intervals for a quantile using batching and sectioning when applying latin hypercube sampling. WSC 2014: 640-651 - 2013
- [c29]James M. Calvin
Confidence intervals for quantiles with standardized time series. WSC 2013: 601-612 - 2012
- [j17]Fang Chu, Marvin K. Nakayama:
Confidence intervals for quantiles when applying variance-reduction techniques. ACM Trans. Model. Comput. Simul. 22(2): 10:1-10:25 (2012) - [c28]Marvin K. Nakayama:
Using sectioning to construct confidence intervals for quantiles when applying importance sampling. WSC 2012: 9:1-9:12 - 2011
- [j16]Josiane Nzouonta, Marvin K. Nakayama, Cristian Borcea:
On deriving and incorporating multihop path duration estimates in VANET protocols. ACM Trans. Model. Comput. Simul. 21(2): 14:1-14:23 (2011) - [c27]Marvin K. Nakayama:
Asymptotic properties of kernel density estimators when applying importance sampling. WSC 2011: 556-568 - [c26]Jose H. Blanchet, Juan Li, Marvin K. Nakayama:
A conditional Monte Carlo method for estimating the failure probability of a distribution network with random demands. WSC 2011: 3837-3848 - 2010
- [c25]Fang Chu, Marvin K. Nakayama:
Confidence intervals for quantiles and value-at-risk when applying importance sampling. WSC 2010: 2751-2761
2000 – 2009
- 2009
- [j15]Srinivasan M. Iyer, Marvin K. Nakayama, Alexandros V. Gerbessiotis:
A Markovian Dependability Model with Cascading Failures. IEEE Trans. Computers 58(9): 1238-1249 (2009) - 2008
- [c24]Marvin K. Nakayama:
Statistical analysis of simulation output. WSC 2008: 62-72 - [c23]Marvin K. Nakayama:
Run-length variability of two-stage multiple comparisons with the best for steady-state simulations and its implications for choosing first-stage run lengths. WSC 2008: 252-259 - 2007
- [j14]Marvin K. Nakayama:
Fixed-width multiple-comparison procedures using common random numbers for steady-state simulations. Eur. J. Oper. Res. 182(3): 1330-1349 (2007) - [c22]Mugdha Khaladkar, Vivian Bellofatto, Jason Tsong-Li Wang, Vandanaben Patel, Marvin K. Nakayama:
Constrained RNA Structural Alignment: Algorithms and Application to Motif Detection in the Untranslated Regions of Trypanosoma brucei mRNAs. BIBE 2007: 334-341 - [c21]Marvin K. Nakayama:
Single-stage multiple-comparison procedure for quantiles and other parameters. WSC 2007: 530-534 - 2006
- [j13]James M. Calvin
Permuted Standardized Time Series for Steady-State Simulations. Math. Oper. Res. 31(2): 351-368 (2006) - [j12]James M. Calvin
The semi-regenerative method of simulation output analysis. ACM Trans. Model. Comput. Simul. 16(3): 280-315 (2006) - [c20]Marvin K. Nakayama:
Output analysis for simulations. WSC 2006: 36-46 - [c19]Marvin K. Nakayama:
Selection and multiple-comparison procedures for regenerative systems. WSC 2006: 287-293 - 2004
- [j11]James M. Calvin
Permuted derivative and importance-sampling estimators for regenerative simulations. Eur. J. Oper. Res. 156(2): 390-414 (2004) - [c18]James M. Calvin, Marvin K. Nakayama:
Permuted Weighted Area Estimators. WSC 2004: 721-727 - 2003
- [c17]Marvin K. Nakayama:
Output analysis: analysis of simulation output. WSC 2003: 49-58 - 2002
- [c16]Marvin K. Nakayama:
Output analysis: simulation output analysis. WSC 2002: 23-34 - [c15]James M. Calvin, Marvin K. Nakayama:
Output analysis: a comparison of output-analysis methods for simulations of processes with multiple regeneration sequences. WSC 2002: 328-335 - 2001
- [j10]Marvin K. Nakayama, Bülent Yener:
Optimal information dispersal for probabilistic latency targets. Comput. Networks 36(5/6): 695-707 (2001) - [j9]Victor F. Nicola, Perwez Shahabuddin, Marvin K. Nakayama:
Techniques for fast simulation of models of highly dependable systems. IEEE Trans. Reliab. 50(3): 246-264 (2001) - [c14]James M. Calvin, Marvin K. Nakayama:
Improving standardized time series methods by permuting path segments. WSC 2001: 348-353 - [c13]James M. Calvin, Peter W. Glynn, Marvin K. Nakayama:
Steady state simulation analysis: importance sampling using the semi-regenerative method. WSC 2001: 441-450 - 2000
- [j8]Sheldon H. Jacobson
A sampling procedure to estimate risk probabilities in access-control security systems. Eur. J. Oper. Res. 122(1): 123-132 (2000) - [j7]James M. Calvin
Central Limit Theorems for Permuted Regenerative Estimators. Oper. Res. 48(5): 776-787 (2000)
1990 – 1999
- 1999
- [j6]Halim Damerdji, Marvin K. Nakayama:
Two-Stage Multiple-Comparison Procedures for Steady State Simulations. ACM Trans. Model. Comput. Simul. 9(1): 1-30 (1999) - [c12]James M. Calvin
On the small-sample optimality of multiple-regeneration estimators. WSC 1999: 655-661 - 1998
- [j5]Marvin K. Nakayama:
On Derivative Estimation of the Mean Time to Failure in Simulations of Highly Reliable Markovian Systems. Oper. Res. 46(2): 285-290 (1998) - [j4]James M. Calvin
Using Permutations in Regenerative Simulations to Reduce Variance. ACM Trans. Model. Comput. Simul. 8(2): 153-193 (1998) - [c11]James M. Calvin, Marvin K. Nakayama:
Exploiting Multiple Regeneration Sequences in Simulation Output Analysis. WSC 1998: 695-700 - 1997
- [c10]Michael Bieber, Michael R. Bartolacci, Jerry Fjermestad, Franz J. Kurfess
Electronic enterprise engineering: an outline of an architecture. ECBS 1997: 376- - [c9]James M. Calvin, Marvin K. Nakayama:
A New Variance-Reduction Technique for Regenerative Simulations of Markov Chains. WSC 1997: 224-229 - 1996
- [c8]Halim Damerdji, Peter W. Glynn, Marvin K. Nakayama, James R. Wilson:
Selecting the Best System in Transient Simulations with Variances Known. WSC 1996: 281-286 - [c7]Halim Damerdji, Marvin K. Nakayama:
Two-Stage Procedures for Multiple Comparisons with a Control in Steady-State Simulations. WSC 1996: 372-375 - 1995
- [c6]Marvin K. Nakayama:
Selecting the Best System in Steady-State Simulations Using Batch Means. WSC 1995: 362-366 - 1994
- [j3]Marvin K. Nakayama, Ambuj Goyal, Peter W. Glynn:
Likelihood Ratio Sensitivity Analysis for Markovian Models of Highly Dependable Systems. Oper. Res. 42(1): 137-157 (1994) - [j2]Marvin K. Nakayama:
A Characterization of the Simple Failure-Biasing Method for Simulations of Highly Reliable Markovian Systems. ACM Trans. Model. Comput. Simul. 4(1): 52-88 (1994) - [c5]Alvin M. Blum, Ambuj Goyal, Philip Heidelberger, Stephen S. Lavenberg, Marvin K. Nakayama, Perwez Shahabuddin:
Modeling and Analysis of System Dependability Using the System Availability Estimator. FTCS 1994: 137-141 - [c4]Marvin K. Nakayama:
Fast simulation methods for highly dependable systems. WSC 1994: 221-228 - 1993
- [j1]Victor F. Nicola, Marvin K. Nakayama, Philip Heidelberger, Ambuj Goyal:
Fast Simulation of Highly Dependable Systems with General Failure and Repair Processes. IEEE Trans. Computers 42(12): 1440-1452 (1993) - [c3]Perwez Shahabuddin, Marvin K. Nakayama:
Estimation of reliability and its derivatives for large time horizons in Markovian systems. WSC 1993: 422-429 - 1992
- [c2]Marvin K. Nakayama:
Efficient Methods for Generating Some Exponentially Tilted Random Variates. WSC 1992: 603-608 - 1990
- [c1]Victor F. Nicola, Marvin K. Nakayama, Philip Heidelberger, Ambuj Goyal:
Fast simulation of dependability models with general failure, repair and maintenance processes. FTCS 1990: 491-498
Coauthor Index
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