TY - JOUR
T1 - Optimal minimax designs for prediction in heteroscedastic models
AU - King, Joy
AU - Wong, Weng Kee
N1 - Funding Information:
The authors would like to thank the associate editor and two referees for comments leading to an improved version of this paper. The research of Wong is partially supported by a NIH FIRST award.
PY - 1998/6/15
Y1 - 1998/6/15
N2 - 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 inciuded.
AB - 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 inciuded.
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U2 - 10.1016/s0378-3758(97)00167-5
DO - 10.1016/s0378-3758(97)00167-5
M3 - Article
AN - SCOPUS:0032525528
SN - 0378-3758
VL - 69
SP - 371
EP - 383
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 2
ER -