Abstract
This chapter considers likelihood-based inference methods for doubly truncated samples under a class of models called the special exponential family (SEF). We introduce specific models in the SEF, and computational algorithms for maximum likelihood estimators (MLEs) under these models. We review the asymptotic theory for the MLE and then give the standard error and confidence interval. We also introduce an R package “double.truncation” (Emura et al, double.truncation: analysis of doubly-truncated data, CRAN 2019) that provides the computational programs to fit doubly truncated data to the models. Finally, we analyze a real dataset for illustration.
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Dörre, A., Emura, T. (2019). Parametric Estimation Under Exponential Family. In: Analysis of Doubly Truncated Data. SpringerBriefs in Statistics(). Springer, Singapore. https://doi.org/10.1007/978-981-13-6241-5_2
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DOI: https://doi.org/10.1007/978-981-13-6241-5_2
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