المؤلفون: Qin, Gengsheng, Jing, Bing-Yi

المصدر: Scandinavian Journal of Statistics, 2001 Dec 01. 28(4), 661-673.

الوصول الحر: https://www.jstor.org/stable/4616688Test

المؤلفون: Li, Ker-Chau, Wang, Jane-Ling, Chen, Chun-Houh

المصدر: The Annals of Statistics, 1999 Feb 01. 27(1), 1-23.

الوصول الحر: https://www.jstor.org/stable/120115Test

المؤلفون: DING, YING, NAN, BIN

المصدر: Scandinavian Journal of Statistics, 2015 Jan 01. 42(2), 397-413.

الوصول الحر: https://www.jstor.org/stable/26593378Test

المؤلفون: Li, Yi, Dicker, Lee, Zhao, Sihai Dave

المصدر: Statistica Sinica, 2014 Jan 01. 24(1), 251-268.

الوصول الحر: https://www.jstor.org/stable/26432542Test

المؤلفون: Nan, Bin, Kalbfleisch, John D., Yu, Menggang

المصدر: The Annals of Statistics, 2009 Oct 01. 37(5A), 2351-2376.

الوصول الحر: https://www.jstor.org/stable/30243708Test

المؤلفون: Nan, Bin, Yu, Menggang, Kalbfleisch, John D.

المصدر: Biometrika, 2006 Dec 01. 93(4), 747-762.

الوصول الحر: https://www.jstor.org/stable/20441325Test

المؤلفون: Eric V. Slud A, Filia Vonta B

المساهمون: The Pennsylvania State University CiteSeerX Archives

المصدر: http://www.math.umd.edu/~evs/myr.html/JSPI05art.pdfTest.

مصطلحات موضوعية: Censored linear regression, Density estimation, Frailty model, Information bound, Least-favorable submodel, Profile likelihood, Semiparametric efficiency, Transformation model

الوصف: www.elsevier.com/locate/jspi Efficient semiparametric estimators via modified profile likelihood

وصف الملف: application/pdf

العلاقة: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.5041Test; http://www.math.umd.edu/~evs/myr.html/JSPI05art.pdfTest

الإتاحة: http://www.math.umd.edu/~evs/myr.html/JSPI05art.pdfTest

المؤلفون: Li, Yi, Dicker, Lee, Zhao, Sihai Dave

المصدر: Harvard University Biostatistics Working Paper Series

مصطلحات موضوعية: Adaptive Dantzig variable selector, Censored linear regression, Buckley-James imputation, Model selection consistency, Asymptotic normality, Microarrays, Statistical Methodology, Statistical Theory, Survival Analysis

وصف الملف: application/pdf

العلاقة: https://biostats.bepress.com/harvardbiostat/paper102Test; https://biostats.bepress.com/cgi/viewcontent.cgi?article=1109&context=harvardbiostatTest

الإتاحة: https://biostats.bepress.com/harvardbiostat/paper102Test
https://biostats.bepress.com/cgi/viewcontent.cgi?article=1109&context=harvardbiostatTest

المؤلفون: Bin Nan, Menggang Yu, John D. Kalbfleisch

المصدر: Ann. Statist. 37, no. 5A (2009), 2351-2376

مصطلحات موضوعية: Statistics and Probability, Wilcoxon signed-rank test, Glivenko–Cantelli class, pseudo Z-estimator, Mathematics - Statistics Theory, nonpredictable weights, Estimating equations, Statistics Theory (math.ST), Accelerated failure time model, semiparametric method, censored linear regression, rank estimating equation, 62N01, FOS: Mathematics, Applied mathematics, Donsker class, 62D05, Mathematics, 62E20, 62N01 (Primary) 62D05 (Secondary), 62E20, empirical processes, Stochastic process, Estimator, Missing data, case-cohort study, Semiparametric model, Statistics, Probability and Uncertainty, Martingale (probability theory)

الوصف: We consider a class of doubly weighted rank-based estimating methods for the transformation (or accelerated failure time) model with missing data as arise, for example, in case-cohort studies. The weights considered may not be predictable as required in a martingale stochastic process formulation. We treat the general problem as a semiparametric estimating equation problem and provide proofs of asymptotic properties for the weighted estimators, with either true weights or estimated weights, by using empirical process theory where martingale theory may fail. Simulations show that the outcome-dependent weighted method works well for finite samples in case-cohort studies and improves efficiency compared to methods based on predictable weights. Further, it is seen that the method is even more efficient when estimated weights are used, as is commonly the case in the missing data literature. The Gehan censored data Wilcoxon weights are found to be surprisingly efficient in a wide class of problems.
Comment: Published in at http://dx.doi.org/10.1214/08-AOS657Test the Annals of Statistics (http://www.imstat.org/aosTest/) by the Institute of Mathematical Statistics (http://www.imstat.orgTest)

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c686160fceb37ac4e7783664c4e5a80bTest

المؤلفون: Leon, Larry, Cai, Tianxi, Wei, L. J.

المصدر: Harvard University Biostatistics Working Paper Series

مصطلحات موضوعية: Censored linear regression, Partial linear model, Resampling method, Rank estimation, Numerical Analysis and Computation, Statistical Methodology, Statistical Models, Statistical Theory, Survival Analysis, stat

الوصف: Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are illustrated with two examples and their validity for cases with practical sample sizes is demonstrated via a simulation study.

العلاقة: https://biostats.bepress.com/harvardbiostat/paper20Test

الإتاحة: https://biostats.bepress.com/harvardbiostat/paper20Test