Bootstrapping the Kalman Filter.

Abstract

The bootstrap is proposed as a method for estimating the precision of forecasts and estimates of parameters of the Kalman Filter model. It is shown that when the system and the filter is in steady state the bootstrap applied to the Gaussian innovations yields asymptotically consistent standard errors. That the bootstrap works well with moderate sample sizes and supplies robustness against departures from normality is substantiated by empirical evidence. Keywords: Bootstrap; Kalman filter; Forecasting; and Robustness.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1984
Accession Number
ADA150509

Entities

People

  • D. S. Stoffer

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Data Science
  • Delphi Method
  • Estimators
  • Filters
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Mathematical Filters
  • New York
  • Precision
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Steady State

Readers

  • Positioning, Navigation, and Timing (PNT) Technology.
  • Regression Analysis.
  • Statistical inference.