We analyze left-truncated and right-censored data using Cox proportional hazard models with long-term survivors. The estimators of covariate coefficients and the long-term survivor proportion are obtained by the parti...We analyze left-truncated and right-censored data using Cox proportional hazard models with long-term survivors. The estimators of covariate coefficients and the long-term survivor proportion are obtained by the partial likelihood method, and their asymptotic properties are also established. Simulation studies demonstrate the performance of the proposed estimators, and an application to a real dataset is provided.展开更多
Under some mild conditions, we derive the asymptotic normality of the Nadaraya-Watson and local linear estimators of the conditional hazard function for left-truncated and dependent data. The estimators were proposed ...Under some mild conditions, we derive the asymptotic normality of the Nadaraya-Watson and local linear estimators of the conditional hazard function for left-truncated and dependent data. The estimators were proposed by Liang and Ould-Sa?d [1]. The results confirm the guess in Liang and Ould-Sa?d [1].展开更多
The paper discusses the regression analysis of current status data,which is common in various fields such as tumorigenic research and demographic studies.Analyzing this type of data poses a significant challenge and h...The paper discusses the regression analysis of current status data,which is common in various fields such as tumorigenic research and demographic studies.Analyzing this type of data poses a significant challenge and has recently gained considerable interest.Furthermore,the authors consider an even more difficult scenario where,apart from censoring,one also faces left-truncation and informative censoring,meaning that there is a potential correlation between the examination time and the failure time of interest.The authors propose a sieve maximum likelihood estimation(MLE)method and in the proposed method for inference,a copula-based procedure is applied to depict the informative censoring.Additionally,the authors utilise the splines to estimate the unknown nonparametric functions in the model,and the asymptotic properties of the proposed estimator are established.The simulation results indicate that the developed approach is effective in practice,and it has been successfully applied to a set of real data.展开更多
Based on the idea of local polynomial double-smoother, we propose an estimator of a conditional cumulative distribution function with dependent and left-truncated data. It is assumed that the observations form a stati...Based on the idea of local polynomial double-smoother, we propose an estimator of a conditional cumulative distribution function with dependent and left-truncated data. It is assumed that the observations form a stationary a-mixing sequence. Asymptotic normality of the estimator is established. The finite sample behavior of the estimator is investigated via simulations.展开更多
基金Natural Science Funds for Distinguished Young Scholar (No. 70825004)Creative Research Groups of China (No. 10721101)+2 种基金Shanghai University of Finance and Economics Project 211 Phase ⅢShanghai Leading Academic Discipline Project (No. B803)Zhou's work was supported by Graduate Creation Funds of Shanghai University of Finance and Economics(No. CXJJ-2011-436)
文摘We analyze left-truncated and right-censored data using Cox proportional hazard models with long-term survivors. The estimators of covariate coefficients and the long-term survivor proportion are obtained by the partial likelihood method, and their asymptotic properties are also established. Simulation studies demonstrate the performance of the proposed estimators, and an application to a real dataset is provided.
基金supported by National Natural Science Foundation of China(No.11301084)Natural Science Foundation of Fujian Province(No.2014J01010)
文摘Under some mild conditions, we derive the asymptotic normality of the Nadaraya-Watson and local linear estimators of the conditional hazard function for left-truncated and dependent data. The estimators were proposed by Liang and Ould-Sa?d [1]. The results confirm the guess in Liang and Ould-Sa?d [1].
基金supported by the National Natural Science Foundation of China under Grant Nos.12171328,12001093,12231011,and 12071176the National Key Research and Development Program of China under Grant No.2020YFA0714102Beijing Natural Science Foundation under Grant No.Z210003。
文摘The paper discusses the regression analysis of current status data,which is common in various fields such as tumorigenic research and demographic studies.Analyzing this type of data poses a significant challenge and has recently gained considerable interest.Furthermore,the authors consider an even more difficult scenario where,apart from censoring,one also faces left-truncation and informative censoring,meaning that there is a potential correlation between the examination time and the failure time of interest.The authors propose a sieve maximum likelihood estimation(MLE)method and in the proposed method for inference,a copula-based procedure is applied to depict the informative censoring.Additionally,the authors utilise the splines to estimate the unknown nonparametric functions in the model,and the asymptotic properties of the proposed estimator are established.The simulation results indicate that the developed approach is effective in practice,and it has been successfully applied to a set of real data.
基金supported by National Natural Science Foundation of China(No.11301084)Natural Science Foundation of Fujian Province(No.2014J01010)
文摘Based on the idea of local polynomial double-smoother, we propose an estimator of a conditional cumulative distribution function with dependent and left-truncated data. It is assumed that the observations form a stationary a-mixing sequence. Asymptotic normality of the estimator is established. The finite sample behavior of the estimator is investigated via simulations.
