This paper considers the monotonic transformation model with an unspecified transformation function and an unknown error function, and gives its monotone rank estimation with length-biased and rightcensored data. The ...This paper considers the monotonic transformation model with an unspecified transformation function and an unknown error function, and gives its monotone rank estimation with length-biased and rightcensored data. The estimator is shown to be√n-consistent and asymptotically normal. Numerical simulation studies reveal good finite sample performance and the estimator is illustrated with the Oscar data set. The variance can be estimated by a resampling method via perturbing the U-statistics objective function repeatedly.展开更多
基金supported by Graduate Innovation Foundation of Shanghai University of Finance and Economics(Grant No.CXJJ2013-451)Cultivation Foundation of Excellent Doctor Degree Dissertation of Shanghai University of Finance and Economics(Grant No.YBPY201504)+4 种基金Program of Educational Department of Fujian Province(Grant Nos.JA14079 and JA12060)Natural Science Foundation of Fujian Province(Grant Nos.2014J01001 and 2012J01028)National Natural Science Foundation of China(Grant No.71271128)the State Key Program of National Natural Science Foundation of China(Grant No.71331006)National Center for Mathematics and Interdisciplinary Sciences,Key Laboratory of Random Complex Structures and Data Science,Chinese Academy of Sciences and Shanghai First-class Discipline A and Innovative Research Team of Shanghai University of Finance and Economics,Program for Changjiang Scholars Innovative Research Team of Ministry of Education(Grant No.IRT13077)
文摘This paper considers the monotonic transformation model with an unspecified transformation function and an unknown error function, and gives its monotone rank estimation with length-biased and rightcensored data. The estimator is shown to be√n-consistent and asymptotically normal. Numerical simulation studies reveal good finite sample performance and the estimator is illustrated with the Oscar data set. The variance can be estimated by a resampling method via perturbing the U-statistics objective function repeatedly.
