THEORY AND APPLICATION OF AN INFERENCE MODEL FOR NON-STATIONARY TIME SERIES MEANS.

Abstract

Complex time series models, such as consumer brand shifting analyses, have required assumptions of parameter stability because statistical models to deal with parameter change were not available. A model is developed here to make inferences about a possibly nonstationary time series mean generating data with Gaussian error. Estimators which are efficient in a special sense are presented, along with examples and suggested applications of the method to brand switching problems. (Author)

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1964
Accession Number
AD0613369

Entities

People

  • John U. Farley
  • Melvin Hinich

Organizations

  • Carnegie Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Consumers
  • Estimators
  • Stationary
  • Switching

Readers

  • Computational Modeling and Simulation
  • Industrial Economics
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference