Bootstrapping Nonlinear Least Squares Estimates in the Kalman Filter Model.

Abstract

The bootstrap is proposed as a method for estimating the precision of forecasts and maximum likelihood estimates of the transition parameters of the Kalman filter model when the estimates are obtained via Newton-Raphson. It is shown that when the system and the filter are in steady state, the bootstrap applied to the Gaussian innovations yields asymptotically consistant standard errors. That the boot strap works well with moderate sample sizes and supplies robustness against departures from normality is substantiated by emperical evidence. Keywords: Parameter estimation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1986
Accession Number
ADA167099

Entities

People

  • David S. Stoffer

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Data Science
  • Estimators
  • Filters
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Multivariate Analysis
  • New York
  • Normality
  • Precision
  • Probability
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Steady State

Fields of Study

  • Mathematics

Readers

  • Inertial Navigation Systems.
  • Regression Analysis.
  • Statistical inference.