Abstract
In order to reduce costs and streamline the efficiency of the cohort research, case-based cohort design is a biased sampling scheme widely used in time event data. If the observation or measurement time is random, the observation process can be regarded as a recurrence process. Due to time and cost constraints, individual tracking can not be carried out indefinitely, which makes the recurrence process impossible to be fully observed, we can only observe the part before the deletion time. The application of Bayesian survival analysis theory to the modeling and analysis of biological and medical data will solve several difficult problems in statistical data analysis, such as small sample size, incomplete data and complex operation environment. The application of Bayesian survival analysis theory in biology and medical statistics enriches and perfects the statistical modeling theory of small sample data. An improved minimization algorithm for constrained estimation is proposed. Simulation studies are carried out to verify the performance of the proposed method in finite samples.
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Lv, X., Wang, C., Qiu, L., Li, H., Feng, L. (2019). The Bayes Quantile Regression Theory and Application. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent, Interactive Systems and Applications. IISA 2018. Advances in Intelligent Systems and Computing, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-02804-6_26
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DOI: https://doi.org/10.1007/978-3-030-02804-6_26
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