TY - JOUR
T1 - An algorithmic construction of optimal minimax designs for heteroscedastic linear models
AU - Brown, Lawrence D.
AU - Wong, Weng Kee
N1 - Funding Information:
The research of Wong is partially supported by a NIH research grant R29 AR44177-01A1.
PY - 2000/4/1
Y1 - 2000/4/1
N2 - We construct optimal designs to minimize the maximum variance of the fitted response over an arbitrary compact region. An algorithm is proposed for finding such optimal minimax designs for the simple linear regression model with heteroscedastic errors. This algorithm always finds the optimal design in a few simple steps. For more complex models where there is a symmetric error variance structure, we suggest a strategy to help find some hitherto elusive optimal minimax designs.
AB - We construct optimal designs to minimize the maximum variance of the fitted response over an arbitrary compact region. An algorithm is proposed for finding such optimal minimax designs for the simple linear regression model with heteroscedastic errors. This algorithm always finds the optimal design in a few simple steps. For more complex models where there is a symmetric error variance structure, we suggest a strategy to help find some hitherto elusive optimal minimax designs.
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U2 - 10.1016/s0378-3758(99)00073-7
DO - 10.1016/s0378-3758(99)00073-7
M3 - Article
AN - SCOPUS:0042207402
SN - 0378-3758
VL - 85
SP - 103
EP - 114
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 1-2
ER -