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pro vyhledávání: '"Yanchun Bao"'
In many observational cohort studies, a pair of correlated event times are usually observed for each individual. This paper develops a new approach for the semiparametric linear transformation model to handle the bivariate survival data under both tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0312576c79723adc6413f41cff952eae
http://hdl.handle.net/11386/4689349
http://hdl.handle.net/11386/4689349
Publikováno v:
Communications in Statistics - Theory and Methods. 42:3819-3837
In survival analysis, time-dependent covariates are usually present as longitudinal data collected periodically and measured with error. The longitudinal data can be assumed to follow a linear mixed effect model and Cox regression models may be used
Publikováno v:
Statistics & Probability Letters. 83:21-27
This paper shows that the maximum likelihood estimate (MLE) for the dispersion parameter of the negative binomial distribution is unique under a certain condition. A fixed-point iteration algorithm is proposed and it guarantees to converge to the MLE
Autor:
Hongsheng Dai, Yanchun Bao
Publikováno v:
Statistics & Probability Letters. 79:1789-1797
An inverse probability weighted estimator is proposed for the joint distribution function of bivariate random vectors under right censoring. The new estimator is based on the idea of transformation of bivariate survival functions and bivariate random
Publikováno v:
Computational Statistics & Data Analysis. 51:6488-6497
The Koul-Susarla-Van Ryzin (KSV) and weighted least squares (WLS) methods are simple to use techniques for handling linear regression models with censored data. They do not require any iterations and standard computer routines can be employed for mod
Publikováno v:
Statistics in medicine. 33(18)
In tumour xenograft experiments, treatment regimens are administered, and the tumour volume of each individual is measured repeatedly over time. Survival data are recorded because of the death of some individuals during the observation period. Also,