Add to ORKGInternational audienceAbstractThere are famous examples of acute sensitivity of optimal parametric procedures to a slight discrepancy from the assumed model.The non parametric approach may also suffer from this kind of deficiency, so that robust procedures (for test and estimation) which are optimal in a neighborhood of a parametric model are to be preferred. This general concept is applied here to survival data analysis
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a param...
Survival analysis is a branch of statistics and biostatistics that studies and compares the survival...
Restricted Mean Survival Time (RMST), the average time without an event of interest until a specific...
International audienceAbstractThere are famous examples of acute sensitivity of optimal parametric p...
Robust statistics is an extension of classical statistics that specifically takes into account the c...
The question of how to compare survival between two or more groups is considered mainly with a view ...
Parametric and semiparametric models are tools with a wide range of applications to reliability, sur...
Robust statistics is an extension of classical parametric statistics that specifically takes into ac...
Although semi- and non-parametric approaches are frequently used to analyse survival data, there are...
In cancer research, one is often interested in the part of the hazard which corresponds to the disea...
This work introduces nonparametric models which are used in time to event data analysis. It is focus...
Traditional survival analysis methods are primarily those of Kaplan-Meier curves, the log-rank test ...
AbstractBerk and Jones (Z. Wahrsch. Verw. Gebiete 47 (1979) 47) described a nonparametric likelihood...
Survival data analysis is a very broad field of statistics, encompassing a large variety of methods ...
In lieu of an abstract, here is the entry\u27s first paragraph: Robust statistics are procedures tha...
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a param...
Survival analysis is a branch of statistics and biostatistics that studies and compares the survival...
Restricted Mean Survival Time (RMST), the average time without an event of interest until a specific...
International audienceAbstractThere are famous examples of acute sensitivity of optimal parametric p...
Robust statistics is an extension of classical statistics that specifically takes into account the c...
The question of how to compare survival between two or more groups is considered mainly with a view ...
Parametric and semiparametric models are tools with a wide range of applications to reliability, sur...
Robust statistics is an extension of classical parametric statistics that specifically takes into ac...
Although semi- and non-parametric approaches are frequently used to analyse survival data, there are...
In cancer research, one is often interested in the part of the hazard which corresponds to the disea...
This work introduces nonparametric models which are used in time to event data analysis. It is focus...
Traditional survival analysis methods are primarily those of Kaplan-Meier curves, the log-rank test ...
AbstractBerk and Jones (Z. Wahrsch. Verw. Gebiete 47 (1979) 47) described a nonparametric likelihood...
Survival data analysis is a very broad field of statistics, encompassing a large variety of methods ...
In lieu of an abstract, here is the entry\u27s first paragraph: Robust statistics are procedures tha...
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a param...
Survival analysis is a branch of statistics and biostatistics that studies and compares the survival...
Restricted Mean Survival Time (RMST), the average time without an event of interest until a specific...