Monte Carlo Analysis of Nonlinear Statistical Models. I. Theory. Revision.

Abstract

Parameter values of nonlinear statistical models are typically estimated from data using iterative numerical procedures. The resulting joint sampling distribution of the parameter estimators is often intractable, resulting in the use of approximators or Monte Carlo simulation to determine properties of the sampling distribution. This paper develops methods, using linear and quadratic approximators as control variates, that reduce the variance of the Monte Carlo estimator by orders of magnitude. Estimation of means, higher order raw moments, variances, covariances, and percentiles is considered. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1985
Accession Number
ADA158613

Entities

People

  • B. R. Schmeiser
  • J. J. Swain

Organizations

  • Purdue University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computational Science
  • Data Science
  • Estimators
  • Industrial Engineering
  • Information Science
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Nonlinear Dynamics
  • Operations Research
  • Sampling
  • Simulations
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Systems Engineering

Fields of Study

  • Mathematics

Readers

  • Statistical inference.