Premium
Robust discrimination designs
Author(s) -
Wiens Douglas P.
Publication year - 2009
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2009.00711.x
Subject(s) - divergence (linguistics) , neighbourhood (mathematics) , computer science , kullback–leibler divergence , regression , linear regression , mathematics , artificial intelligence , machine learning , algorithm , statistics , mathematical analysis , philosophy , linguistics
Summary. We study the construction of experimental designs, the purpose of which is to aid in the discrimination between two possibly non‐linear regression models, each of which might be only approximately specified. A rough description of our approach is that we impose neighbourhood structures on each regression response and determine the members of these neighbourhoods which are least favourable in the sense of minimizing the Kullback–Leibler divergence. Designs are obtained which maximize this minimum divergence. Both static and sequential approaches are studied. We then consider sequential designs whose purpose is initially to discriminate, but which move their emphasis towards efficient estimation or prediction as one model becomes favoured over the other.