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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2191-544X es of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields..978-981-13-6240-8978-981-13-6241-5Series ISSN 2191-544X Series E-ISSN 2191-5458

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Introduction to Double-Truncation, 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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Parametric Estimation Under 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

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Bayesian Inference for Doubly Truncated Data,ocess 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 i

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Linear Regression Under Random Double-Truncation, is argued that the conventional OLS estimator is not valid when truncation is present. Instead, a fundamental property of the regression equation is used to construct a non-parametric plug-in-type estimator. The method is based on the NPMLE which is treated in Chap.?. (see also Efron and Petrosian

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