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Optimal minimax designs for prediction in heteroscedastic models

✍ Scribed by Joy King; Weng Kee Wong

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
680 KB
Volume
69
Category
Article
ISSN
0378-3758

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✦ Synopsis

We construct optimal designs for heteroscedastic models when the goal is to make efficient prediction over a compact interval. It is assumed that the point or points which are interesting to predict are not known before the experiment is run. Two minimax strategies for minimizing the maximum fitted variance and maximum predictive variance across the interval of interest are proposed and, optimal designs are found and compared. An algorithm for generating these designs is included. (~) 1998 Elsevier Science B.V. All rights reserved.

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