TY - JOUR
T1 - A Bayesian design criterion for locating the optimum point on a response surface
AU - Gilmour, Steven G.
AU - Mead, Roger
PY - 2003/9/15
Y1 - 2003/9/15
N2 - Most factorial experiments in industrial research form one stage in a sequence of experiments and so considerable prior knowledge is often available from earlier stages. A Bayesian A-optimality criterion is proposed for choosing designs, when each stage in experimentation consists of a small number of runs and the objective is to optimise a response. Simple formulae for the weights are developed, some examples of the use of the design criterion are given and general recommendations are made.
AB - Most factorial experiments in industrial research form one stage in a sequence of experiments and so considerable prior knowledge is often available from earlier stages. A Bayesian A-optimality criterion is proposed for choosing designs, when each stage in experimentation consists of a small number of runs and the objective is to optimise a response. Simple formulae for the weights are developed, some examples of the use of the design criterion are given and general recommendations are made.
KW - A-optimality
KW - Industrial experimentation
KW - Response surface methods
KW - Sequential design
UR - http://www.scopus.com/inward/record.url?scp=0041928032&partnerID=8YFLogxK
U2 - 10.1016/S0167-7152(03)00154-8
DO - 10.1016/S0167-7152(03)00154-8
M3 - Article
AN - SCOPUS:0041928032
SN - 0167-7152
VL - 64
SP - 235
EP - 242
JO - Statistics & Probability Letters
JF - Statistics & Probability Letters
IS - 3
ER -