Abstract
Doubly truncated lifetimes can arise under time-restricted availability of observational data. In this case, incorporating information on the birth process of units (i.e. the process which describes the emergence of units in the latent population), whose behaviour might change throughout time, is relevant for statistical inference. In this chapter, a Bayesian approach to double-truncation is developed which allows for piecewise constant process intensities. It is described in detail how a valid likelihood function can be developed for this framework. In addition, estimation of the model is explained with numerical suggestions for efficient implementation. The validity of fitted models is assessed via posterior predictive checks. Finally, the method is applied to the dataset of German insolvent companies in order to estimate the latent lifetime distribution and birth process of companies.
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Dörre, A., Emura, T. (2019). Bayesian Inference for Doubly Truncated Data . In: Analysis of Doubly Truncated Data. SpringerBriefs in Statistics(). Springer, Singapore. https://doi.org/10.1007/978-981-13-6241-5_3
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DOI: https://doi.org/10.1007/978-981-13-6241-5_3
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