Design of experiments for locally weighted regression

Valerii V. Fedorov, Grace Montepiedra, Christopher J. Nachtsheim

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


We consider the design of experiments when estimation is to be performed using locally weighted regression methods. We adopt criteria that consider both estimation error (variance) and error resulting from model misspecification (bias). Working with continuous designs, we use the ideas developed in convex design theory to analyze properties of the corresponding optimal designs. Numerical procedures for constructing optimal designs are developed and applied to a variety of design scenarios in one and two dimensions. Among the interesting properties of the constructed designs are the following: (1) Design points tend to be more spread throughout the design space than in the classical case. (2) The optimal designs appear to be less model and criterion dependent than their classical counterparts. (3) While the optimal designs are relatively insensitive to the specification of the design space boundaries, the allocation of supporting points is strongly governed by the points of interest and the selected weight function, if the latter is concentrated in areas significantly smaller than the design region. Some singular and unstable situations occur in the case of saturated designs. The corresponding phenomenon is discussed using a univariate linear regression example.

Original languageEnglish (US)
Pages (from-to)363-382
Number of pages20
JournalJournal of Statistical Planning and Inference
Issue number2
StatePublished - Nov 1 1999

Bibliographical note

Funding Information:
Grace Montepiedra's research was funded by Faculty Research Committee Basic Grant No. BA9644 provided by Bowling Green State University. We would also like to thank the referees for their valuable help in improving the presentation of the manuscript.

Copyright 2017 Elsevier B.V., All rights reserved.


  • D-optimal
  • Equivalence theorem
  • First-order algorithm
  • Linear-optimal
  • Local regression
  • Mean squared error


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