Titlebook: Analysis of Doubly Truncated Data; An Introduction Achim D?rre,Takeshi Emura Book 2019 The Editor(s) (if applicable) and The Author(s), und

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FLACK

Bestehende Erfolgsgrundlagen schwinden, arising from economics, medicine and engineering. After discussing the issues of sampling bias due to double-truncation, we briefly review likelihood-based inference methods for doubly truncated data. We finally compare double-truncation with interval/right censoring.

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Peter Bleses,Britta Busse,Andreas FriemerSEF). 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

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Fresco

Peter Bleses,Britta Busse,Andreas Friemeretric approaches, no specific distributional assumptions are made, and it is described how the estimator originally derived in Efron and Petrosian (J Am Stat Assoc 94(447):824–834, 1999) is defined and motivated. It turns out that the solution to the estimation problem can be regarded as a fixed-poi

Capitulate

GONG

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