Optimum Designs for Second Order Processes with General Linear Means.

Abstract

In the experimentation of any of the sciences, physical, biological, or social there arise situations in which the exact conduct of a specified experiment is under the control of an experimenter. The experimenter must therefore make a decision. Where the conduct of an individual experiment may be costly or where there are a large number of possible experiments a premium is placed upon the quality of this decision. This research was concerned with the selection of the proper experiments when the data are 'time' recordings. Of special interest was data which arise as solutions to linear white noise random differential equations. Approximately optimal designs were effectively characterized for this case. Similar characterizations were shown to hold for partial differential operators and random fields. Work was also begun on the identification of experiments which are robust against departures from the assumed model. Results were obtained for both scalar and 'time' recording data. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 18, 1980
Accession Number
ADA094577

Entities

People

  • Marcus Carlton Spruill Iii

Organizations

  • Georgia Tech Research Corporation

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Data Science
  • Differential Equations
  • Equations
  • Estimators
  • Experimental Design
  • Gaussian Processes
  • Information Science
  • Mathematical Analysis
  • Mathematics
  • Measurement
  • Multivariate Analysis
  • Partial Differential Equations
  • Statistical Analysis
  • Statistics
  • Stochastic Processes
  • White Noise

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Regression Analysis.
  • Systems Analysis and Design