Forecasting the penetration of a new product-a bayesian approach

Scott E. Pammer, Duncan Fong, Steven F. Arnold

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We adopt a Bayesian approach to forecast the penetration of a new product into a market. We incorporate prior information from an existing product and/or management judgments into the data analysis. The penetration curve is assumed to be a nondecreasing function of time and may be under shape constraints. Markov-chain Monte Carlo methods are proposed and used to compute the Bayesian forecasts. An example on forecasting the penetration of color television using the information from black-and-white television is provided. The models considered can also be used to address the general bioassay and reliability stress-testing problems.

Original languageEnglish (US)
Pages (from-to)428-435
Number of pages8
JournalJournal of Business and Economic Statistics
Volume18
Issue number4
DOIs
StatePublished - Jan 1 2000

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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