Bounds on Generalized Linear Predictors with Incomplete Outcome Data

Bounds on Generalized Linear Predictors with Incomplete Outcome Data This paper develops easily computed, tight bounds on Generalized Linear Predictors and instrumental variable estimators when outcome data are partially identified. A salient example is given by Best Linear Predictors under square loss, or Ordinary Least Squares regressions, with missing outcome data, in which case the setup specializes the more general but intractable problem examined by Horowitz et al. [9]. The result is illustrated by re-analyzing the data used in that paper. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

Bounds on Generalized Linear Predictors with Incomplete Outcome Data

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2007 by Springer Science + Business Media B.V.
Subject
Mathematics; Numeric Computing; Mathematical Modeling and Industrial Mathematics; Approximations and Expansions; Computational Mathematics and Numerical Analysis
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1007/s11155-006-9030-5
Publisher site
See Article on Publisher Site

Abstract

This paper develops easily computed, tight bounds on Generalized Linear Predictors and instrumental variable estimators when outcome data are partially identified. A salient example is given by Best Linear Predictors under square loss, or Ordinary Least Squares regressions, with missing outcome data, in which case the setup specializes the more general but intractable problem examined by Horowitz et al. [9]. The result is illustrated by re-analyzing the data used in that paper.

Journal

Reliable ComputingSpringer Journals

Published: Dec 20, 2006

References

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