Statistical Estimation and Decision Techniques Applied to Time Series Analysis for Communication and Tracking Problems.

Abstract

During the period of the contract, contributions were made to the theory and methodology of statistical inference for dynamic models. New procedures for recursive dynamic linear estimation of a trajectory were obtained and theoretically justified; such procedures also have application in communications and physiological monitoring technology. In addition, a new method for forming point estimators was suggested, and shown to have optimal large sample properties. This methodology, and the accompanying theory, have direct application for forming sequential fixed-diameter confidence regions. Still further, estimators and tests of hypotheses were constructed for the parameters of certain structured multivariate models arising in the contexts of human performance evaluation, geology, and/or communication theory. Some distributional representations and approximations were derived which promise to be of use for development and evaluation of inference procedures both in multivariate analysis, and in time series analysis (including the analysis of random fields). (Author)

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1972
Accession Number
AD0749542

Entities

People

  • Leon J. Gleser

Organizations

  • Johns Hopkins University

Tags

DTIC Thesaurus Topics

  • Data Science
  • Estimators
  • Information Science
  • Monitoring
  • Motor Skills
  • Multivariate Analysis
  • Physiological Monitoring
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Estimation
  • Statistical Inference
  • Test And Evaluation
  • Time Series Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms