2022 Winter Simulation Conference (WSC) / Institute of Electrical and Electronics Engineers.

Format:

Book

Author/Creator:

Institute of Electrical and Electronics Engineers, author, issuing body.

Language:

English

Subjects (All):

Big data--Congresses.

Big data.

Digital computer simulation--Congresses.

Digital computer simulation.

Physical Description:

1 online resource

Other Title:

2022 Winter Simulation Conference

Place of Publication:

Piscataway, NJ : IEEE, 2022.

Summary:

Input models that drive stochastic simulations are often estimated from real-world samples of data. This leads to uncertainty in the input models that propagates through to the simulation outputs. Input uncertainty typically refers to the variance of the output performance measure due to the estimated input models. Many methods exist for quantifying input uncertainty when the performance measure is the sample mean of the simulation outputs, however quantiles that are frequently used to evaluate simulation output risk cannot be incorporated into this framework. Here we adapt two input uncertainty quantification techniques for when the performance measure is a quantile of the simulation outputs rather than the sample mean. We implement the methods on two examples and show that both methods accurately estimate an analytical approximation of the true value of input uncertainty.

Contents:

Empirical Uniform Bounds For Heteroscedastic Metamodeling

Estimating Confidence Regions for Distortion Risk Measures and Their Gradients

Overlapping Batch Confidence Regions on the Steady-State Quantile Vector

Robust Simulation Design for Generalized Linear Models in Conditions of Heteroscedasticity or Correlation

Gaussian Processes for High-Dimensional, Large Data Sets: A Review

Sample Average Approximation Over Function Spaces: Statistical Consistency and Rate of Convergence

A Sequential Method for Estimating Steady-State Quantiles Using Standardized Time Series

Tail Quantile Estimation for Non-Preemptive Priority Queues

Input Uncertainty Quantification for Quantiles

Likelihood Ratio Density Estimation for Simulation Models

Density Estimators of the Cumulative Reward Up to a Hitting Time to a Rarely Visited Set of a Regenerative System.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781665476614

1665476613