Kalman Filter Models for Extrapolations in Dose-Response Experiments and Accelerated Life-Tests.

Abstract

Kalman-Filter models with Gaussian innovations provide a useful and easy to implement tool for inference from dose-response experiments and accelerated life-tests. Their main advantage stems from the fact that the system equation of such models allows for the uncertainty and possible changes in a proposed dose-response relationship. This is in contrast to the currently used approaches wherein there is an implicit commitment to the validity of an assumed relationship. In this paper, we overview our recent work in the above general area and suggest avenues for future research. (AN)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 09, 1988
Accession Number
ADA293949

Entities

People

  • Nozer Singpurwalla

Organizations

  • George Washington University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programs
  • Covariance
  • Damage Assessment
  • Data Science
  • Engineering
  • Equations
  • Filters
  • Filtration
  • Gaussian Distributions
  • Information Science
  • Kalman Filters
  • Life Tests
  • Military Research
  • New York
  • Reliability
  • Statistical Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neurotoxicology
  • Systems Analysis and Design

Technology Areas

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