Lifetime Data Anal - Imputation for semiparametric transformation models with biased-sampling data.

Tópicos

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{ data(3008) multipl(1320) sourc(1022) }
{ imag(2830) propos(1344) filter(1198) }
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{ method(2212) result(1239) propos(1039) }
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{ model(3404) distribut(989) bayesian(671) }
{ learn(2355) train(1041) set(1003) }
{ model(2220) cell(1177) simul(1124) }
{ research(1085) discuss(1038) issu(1018) }
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{ result(1111) use(1088) new(759) }
{ decis(3086) make(1611) patient(1517) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
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{ problem(2511) optim(1539) algorithm(950) }
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{ extract(1171) text(1153) clinic(932) }
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{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
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{ imag(2675) segment(2577) method(1081) }
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{ general(901) number(790) one(736) }
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{ studi(1410) differ(1259) use(1210) }
{ system(1050) medic(1026) inform(1018) }
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{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Widely recognized in many fields including economics, engineering, epidemiology, health sciences, technology and wildlife management, length-biased sampling generates biased and right-censored data but often provide the best information available for statistical inference. Different from traditional right-censored data, length-biased data have unique aspects resulting from their sampling procedures. We exploit these unique aspects and propose a general imputation-based estimation method for analyzing length-biased data under a class of flexible semiparametric transformation models. We present new computational algorithms that can jointly estimate the regression coefficients and the baseline function semiparametrically. The imputation-based method under the transformation model provides an unbiased estimator regardless whether the censoring is independent or not on the covariates. We establish large-sample properties using the empirical processes method. Simulation studies show that under small to moderate sample sizes, the proposed procedure has smaller mean square errors than two existing estimation procedures. Finally, we demonstrate the estimation procedure by a real data example.

Resumo Limpo

wide recogn mani field includ econom engin epidemiolog health scienc technolog wildlif manag lengthbias sampl generat bias rightcensor data often provid best inform avail statist infer differ tradit rightcensor data lengthbias data uniqu aspect result sampl procedur exploit uniqu aspect propos general imputationbas estim method analyz lengthbias data class flexibl semiparametr transform model present new comput algorithm can joint estim regress coeffici baselin function semiparametr imputationbas method transform model provid unbias estim regardless whether censor independ covari establish largesampl properti use empir process method simul studi show small moder sampl size propos procedur smaller mean squar error two exist estim procedur final demonstr estim procedur real data exampl

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