Addressing multicollinearity in semiconductor manufacturing

Yu Ching Chang, Christina Mastrangelo

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


When building prediction models in the semiconductor environment, many variables, such as input/output variables, have causal relationships which may lead to multicollinearity. There are several approaches to address multicollinearity: variable elimination, orthogonal transformation, and adoption of biased estimates. This paper reviews these methods with respect to an application that has a structure more complex than simple pairwise correlations. We also present two algorithmic variable elimination approaches and compare their performance with that of the existing principal component regression and ridge regression approaches in terms of residual mean square and R2.

Original languageEnglish
Pages (from-to)843-854
Number of pages12
JournalQuality and Reliability Engineering International
Issue number6
Publication statusPublished - 2011 Oct

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


Dive into the research topics of 'Addressing multicollinearity in semiconductor manufacturing'. Together they form a unique fingerprint.

Cite this