Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys.In addition,some covariates may be subject to measurement errors due to the in...Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys.In addition,some covariates may be subject to measurement errors due to the instrumental contamination,biological variation or other reasons.For the analysis of interval-censored data with mis-measured covariates,the existing methods either assume a parametric model or rely on the availability of replicated surrogate measurements for the error-prone covariate,which both have obvious limitations.To overcome these shortcomings,the authors propose a simulation-extrapolation estimation procedure under a general class of transformation models.The resulting estimators are shown to be consistent and asymptotically normal.The numerical results obtained from a simulation study indicate that the proposed method performs reasonably well in practice.In particular,the proposed method can reduce the estimation bias given by the naive method that does not take measurement errors into account.Finally,the proposed method is applied to a real data set on hypobaric decompression sickness.展开更多
The partially linear single-index model(PLSIM)is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates.This paper considers the PLSIM with measurement error ...The partially linear single-index model(PLSIM)is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates.This paper considers the PLSIM with measurement error possibly in all the variables.The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function.The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method,and apply it to analyze the osteoporosis prevention data.展开更多
基金supported by the National Statistical Science Research Project under Grant No.2022LY041the Nature Science Foundation of Guangdong Province of China under Grant No.2021A1515010044the National Nature Science Foundation of China under Grant No.12071176。
文摘Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys.In addition,some covariates may be subject to measurement errors due to the instrumental contamination,biological variation or other reasons.For the analysis of interval-censored data with mis-measured covariates,the existing methods either assume a parametric model or rely on the availability of replicated surrogate measurements for the error-prone covariate,which both have obvious limitations.To overcome these shortcomings,the authors propose a simulation-extrapolation estimation procedure under a general class of transformation models.The resulting estimators are shown to be consistent and asymptotically normal.The numerical results obtained from a simulation study indicate that the proposed method performs reasonably well in practice.In particular,the proposed method can reduce the estimation bias given by the naive method that does not take measurement errors into account.Finally,the proposed method is applied to a real data set on hypobaric decompression sickness.
基金the National Natural Science Foundation of China under Grant Nos.11971171,11971300,11901286,12071267 and 12171310the Shanghai Natural Science Foundation under Grant No.20ZR1421800+2 种基金the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University)the General Research Fund(HKBU12303421,HKBU12303918)the Initiation Grant for Faculty Niche Research Areas(RC-FNRA-IG/20-21/SCI/03)of Hong Kong Baptist University。
文摘The partially linear single-index model(PLSIM)is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates.This paper considers the PLSIM with measurement error possibly in all the variables.The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function.The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method,and apply it to analyze the osteoporosis prevention data.
