Density Estimation of Simulation Output Using Exponential EPI-Splines

Abstract

The density of stochastic simulation output provides more information on system performance than the mean alone. However, density estimation methods may require large sample sizes to achieve a certain accuracy or desired structural properties. A nonparametric estimation method based on exponential epi-splines has shown promise to overcome this difficulty by incorporating qualitative and quantitative information that reduces the space of possible density estimates substantially. Such "soft" information may come in the form of the knowledge of a non-negative support, unimodality, and monotonicity, and is often available in simulation applications. We examine this method for output analysis of stochastic systems with fixed input parameters, and for a model with stochastic input parameters, with an emphasis on the use of derivative information.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2013
Accession Number
ADA604705

Entities

People

  • Dashi I. Singham
  • Roger J. Wets

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Computations
  • Continuity
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Intervals
  • Mathematics
  • Operations Research
  • Probability
  • Random Variables
  • Statistical Analysis
  • Stochastic Processes
  • Uncertainty

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space