ࡱ> Root Entry#NFileHeaderYDocInfoH-BodyText #N߃#N !"#$%&'()*+Root Entry#NFileHeaderYDocInfoH-BodyText #N߃#N  ] ȩ : Regression analysis with right censored data Ŭ : 1 P [\mxmYP ĬY] | : 5 20|(”|) $ 5:00-6:00 nj :  6218 In this talk, we are concerned with estimation of the varying coefficient regression model. The model is a useful alternative to the classical linear model, since the former model is much richer and more flexible than the latter. We propose estimators of the coefficient functions for the varying coefficient model in the case where different coefficient functions depend on different covariates and the response is subject to random right censoring. Since our model has an additive structure and requires multivariate smoothing we employ a smooth backfitting technique, that is known to be an effective way to avoid ``the curse of dimensionality" in structured nonparametric models. The estimators are based on synthetic data obtained by an unbiased transformation. The asymptotic normality of the estimators is established and a numerical study illustrates the reliability of our esGIF89aɻxxxkkk]]]PPPCCC555((( d`LH00`Hذ̘`H0|pdXL@`0H `H0`dHL`H0xp`XH@0d LȰ``HH00dLؘȀ`H0xp`XH@d0L `H0d`LH`H0|pdXL@0`$H`dHL00`HذȘdL0xp`XH@0` HdH0ȳ``HHdL0xp`XH@`0H Ը̰ĠऀؘpȈXxHp@h@d8`8X0xT0pH dxddxd`d`p`|``x`p``x`!, H*\ȰÇ#JHŋ3jȱǏ CIɓ(S\ɲ˗0cʜI͛8sɳϟ@ JѣH*]ʴӧPJJիXjʵׯ`ÊKٳhӪ]˶۷<3e(w?t;._zc//E̸ǐ#KL2#мKo聠KMZtҬU Mmڭ칷gӱYkḅ&qw={b/kν y x˓_/0yݓ|z^}'~^]~)؟'Uh!N7^>@("!T+X|.(x#4#d:BC^hK}J>$ Xf\v`)dڔ|gȠIހهo&fy82yg{uJ'矃ޗ蠀 s:dNZ榈))|'ҧjxzLyjz'qjV!5&KrR(~jފ,Ik$N撋.>6ke9kꦿ )k+KZ~*o&s *]O=jںJ*%rq1첥k6L3++l$7V*qGLo/Gk2<4,-u594CsOTBw;7PGry&(;W̨nȚM6R_,yqSG-ǧms'1=x99`s~*sO"sy贎FSՅ+x-wW'׌7}l;sOc}r3g%6w3R] ߡ6%"3V˝t=Kp\0jҝ !H{̪Rm!,MK"0>YuRf* f+W!.z` H2hL6pH:x̣> IBL"F:򑐌$'IJZ̤&7Nz (GIRL*WV򕰌,gIZv2 ;.w^ 0Ib2 ;`a``a`0x  ` l x]ajou2014D 9 22| Ɣ| $ 1:29:43ajou8, 5, 8, 1453 WIN32LEWindows_7@P'@߃#N@Ak@3J襽iAKTP?ZB[EB=eYZуDdMj}m7/d2;UB ~h+[#pHSa TهsuƇ]YB;X2^bU݅8|?I-%09'bWh`5%cKCߗw}"Ei-?`&lUGʢZUu?VT39hLؗ!$Hh *oP/ǃ#TF=QI^@. *qfJ1wk'9,짷Eh]|ޏw\[wWϝJXg=Veop*vaZկS;9ré>ϖ-gF_~Koߵ?LHWP Document Fileu@DO 6C,H,#GB!k0~ 6*7=M*Z:ҙ#?*?ӿT@Y~X+mj3'm[v|:j)%C$Mϼ5#cd߈e of dimensionality" in structured nonparametric models. The estimators are based on synthetic data obtained by an unbiased transformation. The asymptotic normality of the estimators is established and a numerical study illustrates the reliability of our esVOhe;iWD*V%AM`H-ChR4ٙIL3Ie *^"hש a[ RWK}0P113HwJDӨ~N.Y)2}5GOi76o@RC{%NG税gBxTy$Fuaa`58FC!~qA_35}q0G!h,6_-]Y zstqtu=>dw^<~wqi5{[)i1=*p-Ņ[Et֞˫ k}^Vb0=KGdĸ Y\Tu<9Ի3ή 5/>q>o:4x3?>;52vl-^_q؟ѷ1HaAS]cKEjHq\' NrZוq'L3wGw:޺>QaV㎈;+ Ѯ4̲y)у8HuW=׮KkxUfΔk6h#yʣHpkt7)[,:UW;kKE'5G4i|6V;l5 rs]u9\QMU/B#mN1:L s1 83[2]&TqFh-m:'l==Nek%\ѩ}p<"/ƨqZ{IFCX.yp֦k.x7DGLeth\=!Korŕ2\ڊx@.[tVewoGY <**Dzb`g&bzwđĬ(<]Q)','>ݒq;+b3-;;w/x;@׊]ԍ4̴-6ڲԾ3,ճDd<$'pYc^茤dJO,J GQmO|nNT|~)Τ`f 3rZgn1wZ*ړ;]Ye,ۊ2v(e?;E}_f.\\ߘ})_%J3gTHwpSummaryInformation.@PrvImage PrvTextDocOptions #N#NScripts #N#NJScriptVersion ` DefaultJScript]_LinkDoca Section0j !"#$%&'()*+,-./0123456789:;<=>?ABCDEFGIJKLMNOPQRSTUVWXZ[\^_bcdefghiklmnopqrstuvwxyz{|} !"#$%&'()*+,-./0123456789:;<=>?ABCDEFGIJKLMNOPQRSTUVWXZ[\^_bcdefghiklmnopqrstuvwxyz{|}HwpSummaryInformation.@PrvImage PrvTextDocOptions #N#NSection0jScripts #N#NJScriptVersion ` DefaultJScript]_LinkDoca  ] ȩ : Regression analysis with right censored data Ŭ : 1 P [\mxmYP ĬY] | : 5 20|(”|) $ 5:00-6:00 nj :  6218 In this talk, we are concerned with estimation of the varying coefficient regression model. The model is a useful alternative to the classical linear model, since the former model is much richer and more flexible than the latter. We propose estimators of the coefficient functions for the varying coefficient model in the case where different coefficient functions depend on different covariates and the response is subject to random right censoring. Since our model has an additive structure and requires multivariate smoothing we employ a smooth backfitting technique, that is known to be an effective way to avoid ``the curse of dimensionality" in structured nonparametric models. The estimators are based on synthetic data obtained by an unbiased transformation. The asymptotic normality of the estimators is established and a numerical study illustrates the reliability of our esGIF89aɻxxxkkk]]]PPPCCC555((( d`LH00`Hذ̘`H0|pdXL@`0H `H0`dHL`H0xp`XH@0d LȰ``HH00dLؘȀ`H0xp`XH@d0L `H0d`LH`H0|pdXL@0`$H`dHL00`HذȘdL0xp`XH@0` HdH0ȳ``HHdL0xp`XH@`0H Ը̰ĠऀؘpȈXxHp@h@d8`8X0xT0pH dxddxd`d`p`|``x`p``x`!, H*\ȰÇ#JHŋ3jȱǏ CIɓ(S\ɲ˗0cʜI͛8sɳϟ@ JѣH*]ʴӧPJJիXjʵׯ`ÊKٳhӪ]˶۷<3e(w?t;._zc//E̸ǐ#KL2#мKo聠KMZtҬU Mmڭ칷gӱYkḅ&qw={b/kν y x˓_/0yݓ|z^}'~^]~)؟'Uh!N7^>@("!T+X|.(x#4#d:BC^hK}J>$ Xf\v`)dڔ|gȠIހهo&fy82yg{uJ'矃ޗ蠀 s:dNZ榈))|'ҧjxzLyjz'qjV!5&KrR(~jފ,Ik$N撋.>6ke9kꦿ )k+KZ~*o&s *]O=jںJ*%rq1첥k6L3++l$7V*qGLo/Gk2<4,-u594CsOTBw;7PGry&(;W̨nȚM6R_,yqSG-ǧms'1=x99`s~*sO"sy贎FSՅ+x-wW'׌7}l;sOc}r3g%6w3R] ߡ6%"3V˝t=Kp\0jҝ !H{̪Rm!,MK"0>YuRf* f+W!.z` H2hL6pH:x̣> IBL"F:򑐌$'IJZ̤&7Nz (GIRL*WV򕰌,gIZv2 ;.w^ 0Ib2 ;`a``a`0x  ` l x]ajou2014D 9 22| Ɣ| $ 1:29:43ajou8, 5, 8, 1453 WIN32LEWindows_7@P'@߃#N@Ak@3J襽iAKTP?ZB[EB=eYZуDdMj}m7/d2;UB ~h+[#pHSa TهsuƇ]YB;X2^bU݅8|?I-%09'bWh`5%cKCߗw}"Ei-?`&lUGʢZUu?VT39hLؗ!$Hh *oP/ǃ#TF=QI^@. *qfJ1wk'9,짷Eh]|ޏw\[wWϝJXg=Veop*vaZկS;9ré>ϖ-gF_~Koߵ?LHWP Document Fileu@DO 6C,H,#GB!k0~ 6*7=M*Z:ҙ#?*?ӿT@Y~X+mj3'm[v|:j)%C$Mϼ5#cd߈e of dimensionality" in structured nonparametric models. The estimators are based on synthetic data obtained by an unbiased transformation. The asymptotic normality of the estimators is established and a numerical study illustrates the reliability of our esVOhe;iWD*V%AM`H-ChR4ٙIL3Ie *^"hש a[ RWK}0P113HwJDӨ~N.Y)2}5GOi76o@RC{%NG税gBxTy$Fuaa`58FC!~qA_35}q0G!h,6_-]Y zstqtu=>dw^<~wqi5{[)i1=*p-Ņ[Et֞˫ k}^Vb0=KGdĸ Y\Tu<9Ի3ή 5/>q>o:4x3?>;52vl-^_q؟ѷ1HaAS]cKEjHq\' NrZוq'L3wGw:޺>QaV㎈;+ Ѯ4̲y)у8HuW=׮KkxUfΔk6h#yʣHpkt7)[,:UW;kKE'5G4i|6V;l5 rs]u9\QMU/B#mN1:L s1 83[2]&TqFh-m:'l==Nek%\ѩ}p<"/ƨqZ{IFCX.yp֦k.x7DGLeth\=!Korŕ2\ڊx@.[tVewoGY <**Dzb`g&bzwđĬ(<]Q)','>ݒq;+b3-;;w/x;@׊]ԍ4̴-6ڲԾ3,ճDd<$'pYc^茤dJO,J GQmO|nNT|~)Τ`f 3rZgn1wZ*ړ;]Ye,ۊ2v(e?;E}_f.\\ߘ})_%J3gT