Add to ORKGThe recently developed conditional Gaussian diffusion process model is a powerful tool of survival analysis. Its generality not only encompasses the survival models to date but also brings into focus the influence of unobserved variables related to "death" of individuals. Further, that the model makes feasible a unique estimation of the parameters of the underlying unobserved or partially observed process is shown in this paper through a set of simulated data on death times and an unobserved variable. Possibilities of extensive use of the model to areas other than mortality are pointed out
We consider a range of models that may be used to predict the future persistence of populations, par...
In biomedical studies, researchers are often interested in the relationship between patients' charac...
We consider Bayesian hierarchical models for event history analysis, where the event times are mode...
In biostatistical, epidemiological and demographic studies of human survival it is often necessary t...
AbstractIn biostatistical, epidemiological and demographic studies of human survival it is often nec...
A number of multivariate stochastic process models have been developed to represent human physiologi...
This paper describes the stochastic process model for mortality rates of the population. The key que...
The central statistical problem of survival analysis is to determine and characterize the conditiona...
Survival analysis is an old area of statistics dedicated to the study of time-to-event random variab...
Various multivariate stochastic process models have been developed to represent human physiological ...
One of the interesting directions of research in IIASA's Population Program deals with the methodolo...
The paper is devoted to the analysis of stochastic process models of mortality which can explain bot...
The study of events involving an element of time has a long and important history in statistical res...
The first part of this thesis deals with exact simulation of multidimensional diffusion processes. T...
AbstractLet Y=(Yt)t≥0) be an unobserved random process which influences the distribution of a random...
We consider a range of models that may be used to predict the future persistence of populations, par...
In biomedical studies, researchers are often interested in the relationship between patients' charac...
We consider Bayesian hierarchical models for event history analysis, where the event times are mode...
In biostatistical, epidemiological and demographic studies of human survival it is often necessary t...
AbstractIn biostatistical, epidemiological and demographic studies of human survival it is often nec...
A number of multivariate stochastic process models have been developed to represent human physiologi...
This paper describes the stochastic process model for mortality rates of the population. The key que...
The central statistical problem of survival analysis is to determine and characterize the conditiona...
Survival analysis is an old area of statistics dedicated to the study of time-to-event random variab...
Various multivariate stochastic process models have been developed to represent human physiological ...
One of the interesting directions of research in IIASA's Population Program deals with the methodolo...
The paper is devoted to the analysis of stochastic process models of mortality which can explain bot...
The study of events involving an element of time has a long and important history in statistical res...
The first part of this thesis deals with exact simulation of multidimensional diffusion processes. T...
AbstractLet Y=(Yt)t≥0) be an unobserved random process which influences the distribution of a random...
We consider a range of models that may be used to predict the future persistence of populations, par...
In biomedical studies, researchers are often interested in the relationship between patients' charac...
We consider Bayesian hierarchical models for event history analysis, where the event times are mode...