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- W2945118406 abstract "We investigate the well-known linear regression model under double-truncation, i.e. the response variable is subjected to random double-truncation. It 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. 4 (see also Efron and Petrosian in J Am Stat Assoc 94(447):824–834, 1999; Shen in Ann Inst Stat Math 62:835–853, 2010). It is described how the estimator and related aspects can be estimated and how standard errors can be approximated via a bootstrap procedure. Asymptotic properties including consistency and asymptotic normality are stated. The method is finally applied to a dataset of German insolvent companies which was introduced in Chap. 1." @default.
- W2945118406 created "2019-05-29" @default.
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- W2945118406 date "2019-01-01" @default.
- W2945118406 modified "2023-09-25" @default.
- W2945118406 title "Linear Regression Under Random Double-Truncation" @default.
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- W2945118406 doi "https://doi.org/10.1007/978-981-13-6241-5_5" @default.
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