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Loss functions and estimation

  • Douglas A. Schroeder
Chapter
Part of the Springer Series in Accounting Scholarship book series (KLAS, volume 5)

Abstract

In the previous chapter we reviewed some results of linear (least squares) models without making the loss function explicit. In this chapter we remedy this and extend the discussion to various other (sometimes referred to as "robust") approaches. That the loss function determines the properties of estimators is common to classical and Bayesian statistics (whether made explicit or not). We’ll review a few loss functions and the associated expected loss minimizing estimators. Then we briefly review maximum likelihood estimation (MLE) and nonlinear regression.

Keywords

Maximum Likelihood Estimation Posterior Distribution Loss Function Information Matrix Discrete Choice Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.The Ohio State UniversityColumbusUSA

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