Optimal binning strategies under squared error loss in selective assembly with measurement error

Shun Matsuura, Nobuo Shinozaki

Research output: Contribution to journalArticle

20 Citations (Scopus)


Selective assembly is an effective approach for improving a quality of a product assembled from two types of components, when the quality characteristic is the clearance between the mating components. Mease et al. (2004) have extensively studied optimal binning strategies under squared error loss in selective assembly, especially for the case when two types of component dimensions are identically distributed. However, the presence of measurement error in component dimensions has not been addressed. Here we study optimal binning strategies under squared error loss when measurement error is present. We give the equations for the optimal partition limits minimizing expected squared error loss, and show that the solution to them is unique when the component dimensions and the measurement errors are normally distributed. We then compare the expected losses of the optimal binning strategies with and without measurement error for normal distribution, and also evaluate the influence of the measurement error.

Original languageEnglish
Pages (from-to)2863-2876
Number of pages14
JournalCommunications in Statistics - Theory and Methods
Issue number16
Publication statusPublished - 2007 Dec 1



  • Match gauging
  • Measurement error
  • Normal distribution
  • Selective assembly
  • Squared error loss

ASJC Scopus subject areas

  • Statistics and Probability

Cite this