ABSTRACT
Beal, S. L. imd Sheiner, L. B. (1988). Heterosccdastic nonlinear regression. Tcchno,rnetr.%cs 3 Bickcl, P. J . (1973). On some arialogucs t,o linear conihinatior~s of order statistics in the lineitr niodcl. ilma1.s oJ' SLa1,istics 1, 597 616. I3icke1, P. J . (1975). One-stcp Hltbcr cstirnatcs in the h e a r model. Journml oJ' the Amcrlccm Statisi,icu,l llssoc~iaiion Carroll, R.. J . and Ruppcrt, D. (1982). Robust eroscedastic linear r~iodels. Annals of Stn2istics Dutter, R. (1975). R,obust regression: different, iipproi~ches to numerical solutions urld algorithms. Research rcport 6. Facl-igruppe fuer statistik, ETH, Zurich. Gallmt, A. R. (1975). Seemingly unrela.t;cd liorilincar regressions. , J O I L L ~ . ~ ~ L ~ 01 Econorrrclr.ics 3, 3 5 50. Gallant, A. R. (1987). No.rzll;neal. ,"P'lnzst.ical Models. Jo11r1 Wilcy 6LSons, New York. Genni~ig, C., Chinchilli, V. M,, arid Carter, W. H. (1989). Response surface analysis with correlated data: A nonlinear model approach. Jour.rt,al of h e American SLatisticul ilssociatior~, Giltinan, D. M . and Ruppert, D. (1989). Fitting l-ietcroscetlastic reglessiori rnodels to indivitlual pharrnacoki~ietic data using stantlard
11. Hartley, 11. 0. (1961). 'I'he rrloclified Gauss-Kewtoti ~rretlrotl for the fitting of nonlinear regression f~mctions by least squares. 7'echnornctrics 3, 269-280.
