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Marvin K. Nakayama

Publication record assembled from the DBLP archive of ranked conferences.

Papers indexed

46

Venues

3

Active years

1992–2025

Best venue rank

National

Where they publish

Papers

46 indexed papers, newest first.

YearVenueTitleAuthors
2025WSCComputing Estimators of a Quantile and Conditional Value-at-Risk.Sha Cao, Truong Dang, James M. Calvin, Marvin K. Nakayama
2025WSCCentral Limit Theorem for a Randomized Quasi-Monte Carlo Estimator of a Smooth Function of Means.Marvin K. Nakayama, Bruno Tuffin, Pierre L'Ecuyer
2024WSCSome Asymptotic Regimes for Quantile Estimation.Marvin K. Nakayama, Bruno Tuffin
2023WSCConfidence Intervals for Randomized Quasi-Monte Carlo Estimators.Pierre L'Ecuyer, Marvin K. Nakayama, Art B. Owen, Bruno Tuffin
2023WSCEfficiency of Estimating Functions of Means in Rare-Event Contexts.Marvin K. Nakayama, Bruno Tuffin
2022WSCDensity Estimators of the Cumulative Reward Up to a Hitting Time to a Rarely Visited Set of a Regenerative System.Marvin K. Nakayama, Bruno Tuffin
2021WSCSufficient Conditions for a Central Limit Theorem to Assess the Error of Randomized Quasi-Monte Carlo Methods.Marvin K. Nakayama, Bruno Tuffin
2020WSCComparing Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set.Peter W. Glynn, Marvin K. Nakayama, Bruno Tuffin
2020WSCQuantile Estimation Via a Combination of Conditional Monte Carlo and Randomized Quasi-Monte Carlo.Marvin K. Nakayama, Zachary T. Kaplan, Yajuan Li, Bruno Tuffin, Pierre L'Ecuyer
2019WSCRandomized Quasi-Monte Carlo for Quantile Estimation.Zachary T. Kaplan, Yajuan Li, Marvin K. Nakayama, Bruno Tuffin
2019WSCEfficient Estimation of the Mean Hitting Time to a set of A Regenerative System.Marvin K. Nakayama, Bruno Tuffin
2018WSCUsing Regenerative simulation to Calibrate exponential Approximations to Risk Measures of Hitting times to rarely Visited Sets.Peter W. Glynn, Marvin K. Nakayama, Bruno Tuffin
2018WSCMonte Carlo estimation of Economic Capital.Zachary T. Kaplan, Yajuan Li, Marvin K. Nakayama
2017WSCHistory of improving statistical efficiency.Russell R. Barton, Marvin K. Nakayama, Lee Schruben
2017WSCQuantile estimation using conditional Monte Carlo and Latin hypercube sampling.Hui Dong, Marvin K. Nakayama
2017WSCOn the estimation of the mean time to failure by simulation.Peter W. Glynn, Marvin K. Nakayama, Bruno Tuffin
2016WSCVariance reduction for estimating a failure probability with multiple criteria.Andres Alban, Hardik A. Darji, Atsuki Imamura, Marvin K. Nakayama
2015WSCEstimating a failure probability using a combination of variance-reduction techniques.Marvin K. Nakayama
2014WSCConstructing confidence intervals for a quantile using batching and sectioning when applying latin hypercube sampling.Hui Dong, Marvin K. Nakayama
2014SIMULTECHQuantile estimation when applying conditional Monte Carlo.Marvin K. Nakayama
2013WSCConfidence intervals for quantiles with standardized time series.James M. Calvin, Marvin K. Nakayama
2012WSCUsing sectioning to construct confidence intervals for quantiles when applying importance sampling.Marvin K. Nakayama
2011WSCA conditional Monte Carlo method for estimating the failure probability of a distribution network with random demands.Jose H. Blanchet, Juan Li, Marvin K. Nakayama
2011WSCAsymptotic properties of kernel density estimators when applying importance sampling.Marvin K. Nakayama
2010WSCConfidence intervals for quantiles and value-at-risk when applying importance sampling.Fang Chu, Marvin K. Nakayama
2008WSCStatistical analysis of simulation output.Marvin K. Nakayama
2008WSCRun-length variability of two-stage multiple comparisons with the best for steady-state simulations and its implications for choosing first-stage run lengths.Marvin K. Nakayama
2007BIBEConstrained RNA Structural Alignment: Algorithms and Application to Motif Detection in the Untranslated Regions of Trypanosoma brucei mRNAs.Mugdha Khaladkar, Vivian Bellofatto, Jason Tsong-Li Wang, Vandanaben Patel, Marvin K. Nakayama
2007WSCSingle-stage multiple-comparison procedure for quantiles and other parameters.Marvin K. Nakayama
2006WSCOutput analysis for simulations.Marvin K. Nakayama
2006WSCSelection and multiple-comparison procedures for regenerative systems.Marvin K. Nakayama
2004WSCPermuted Weighted Area Estimators.James M. Calvin, Marvin K. Nakayama
2003WSCOutput analysis: analysis of simulation output.Marvin K. Nakayama
2002WSCOutput analysis: a comparison of output-analysis methods for simulations of processes with multiple regeneration sequences.James M. Calvin, Marvin K. Nakayama
2002WSCOutput analysis: simulation output analysis.Marvin K. Nakayama
2001WSCSteady state simulation analysis: importance sampling using the semi-regenerative method.James M. Calvin, Peter W. Glynn, Marvin K. Nakayama
2001WSCImproving standardized time series methods by permuting path segments.James M. Calvin, Marvin K. Nakayama
1999WSCOn the small-sample optimality of multiple-regeneration estimators.James M. Calvin, Peter W. Glynn, Marvin K. Nakayama
1998WSCExploiting Multiple Regeneration Sequences in Simulation Output Analysis.James M. Calvin, Marvin K. Nakayama
1997WSCA New Variance-Reduction Technique for Regenerative Simulations of Markov Chains.James M. Calvin, Marvin K. Nakayama
1996WSCSelecting the Best System in Transient Simulations with Variances Known.Halim Damerdji, Peter W. Glynn, Marvin K. Nakayama, James R. Wilson
1996WSCTwo-Stage Procedures for Multiple Comparisons with a Control in Steady-State Simulations.Halim Damerdji, Marvin K. Nakayama
1995WSCSelecting the Best System in Steady-State Simulations Using Batch Means.Marvin K. Nakayama
1994WSCFast simulation methods for highly dependable systems.Marvin K. Nakayama
1993WSCEstimation of reliability and its derivatives for large time horizons in Markovian systems.Perwez Shahabuddin, Marvin K. Nakayama
1992WSCEfficient Methods for Generating Some Exponentially Tilted Random Variates.Marvin K. Nakayama