Statistical Models

Abstract

Efficient planning requires efficient methods for forecasting and control. One of the objects of the present research is to further extend methods described in a recent successful 550 page book by Box and Jenkins developed under AFOSR sponsorship. Non-stationary models which can adequately represent multiple dependent records developing in time have been obtained and efficient methods for identification, estimation and diagnostic checking have been studied. Of particular importance are canonical forms of the model whereby the information cantained in many records can often be summarized in a few composite series. Difficult problems in estimation are being approached using Bayesian methods. Two problems occurring in continuous time control theory were studied and results obtained on the accuracy properties of numerical algorithms for solving them approximately. Some new results in Bayesian Tolerance Regions have been obtained.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1971
Accession Number
AD0735197

Entities

People

  • George E. Box

Organizations

  • University of Wisconsin Madison Department of Statistics

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Bayesian Networks
  • Control Theory
  • Data Science
  • Delphi Method
  • Distribution Theory
  • Economic Forecasting
  • Equations
  • Information Science
  • Integral Equations
  • Inventory Control
  • Models
  • Probability
  • Scientific Research
  • Statistical Analysis
  • Universities

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.
  • Technical Research and Report Writing.

Technology Areas

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