Prediction variance of a central composite design with missing observation

Kohei Fujiwara, Shun Matsuura

Research output: Contribution to journalArticle

Abstract

Central composite design has been widely used in response surface methods. This article studies how much the variance of a predicted response is inflated when an observation is missing in a central composite design. A mathematical expression is derived for the inflation amount of the prediction variance. It turns out that, for rotatable central composite designs, the inflation amount of the prediction variance depends only on the Euclidean norms and the inner product of the two vectors of factor values at which the observation is missing and the response is predicted. Several numerical examples are presented to show relationships between the inflation amount of the prediction variance and the angle formed by the two vectors.

Original languageEnglish
JournalCommunications in Statistics - Theory and Methods
DOIs
Publication statusPublished - 2019 Jan 1

    Fingerprint

Keywords

  • Central composite design
  • missing data
  • prediction variance
  • response surface method
  • rotatable design

ASJC Scopus subject areas

  • Statistics and Probability

Cite this