{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T08:00:36Z","timestamp":1791187236057,"version":"4.1.0"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[1993,9,1]],"date-time":"1993-09-01T00:00:00Z","timestamp":746841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[1993,9,1]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n               <jats:p>This paper addresses the issue of constructing large sample G-optimal designs when the variability of the response varies across a compact design space. A useful characterization theorem is presented along with a computer algorithm for generating (heteroscedastic) G-optimal designs. to facilitate comparisons between D- and G-optimal designs, Atwood's inequality for comparing D- and G-efficiencies in homoscedastic models is generalized to heteroscedastic models. Some robustness properties of these designs are presented.<\/jats:p>","DOI":"10.1111\/j.2517-6161.1993.tb01946.x","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T06:30:28Z","timestamp":1544164228000},"page":"871-880","source":"Crossref","is-referenced-by-count":24,"title":["Heteroscedastic G-Optimal Designs"],"prefix":"10.1093","volume":"55","author":[{"given":"Weng Kee","family":"Wong","sequence":"first","affiliation":[{"name":"University of California , Los Angeles , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. 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