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- W1482288042 abstract "Publisher Summary To ease the estimation problem and to estimate the survival probabilities, this chapter proposes a state-space modeling approach by combining stochastic models with statistical models. The Gibbs sampling method and the Markov Chain and Monte Carlo approach (MCMC) can be readily applied to estimate the unknown parameters and the state variables. By using these estimates, the model can be validated and the survival probabilities can be estimated. The chapter illustrates the model and the method by using a birth–death–immigration–illness–cure process that involves stochastic birth–death processes with immigration and the illness and cure processes for a disease such as tuberculosis. It extends this modeling approach to other human diseases such as tuberculosis. This type of modeling approach is definitely useful for other diseases, such as heart disease, and the risk assessment of environmental agents." @default.
- W1482288042 created "2016-06-24" @default.
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- W1482288042 date "2003-01-01" @default.
- W1482288042 modified "2023-09-28" @default.
- W1482288042 title "State Space Models for Survival Analysis" @default.
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- W1482288042 doi "https://doi.org/10.1016/s0169-7161(03)23029-7" @default.
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