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Minimum Risk Regression.pdf

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Preview of Minimum Risk Regression
🔗 Source: epub.ub.uni-muenchen.de
📊 Size: 194 KB
👤 Author: Toutenburg; Shalabh
⬇️ Downloads: 131

Summary

Toutenburg and Shalabh discuss linear regression models with missing observations on the response variable, presenting two estimators of regression coefficients using the minimum risk approach. They analyze the asymptotic properties of these estimators and derive conditions for their superiority, which are easy to check in practice. The estimators, b1 and b2, are biased but have smaller mean squared errors than the traditional unbiased estimator under certain conditions. The authors use small disturbance asymptotic theory to approximate the bias vectors and mean squared error matrices of b1 and b2.

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Description

Toutenburg and Shalabh discuss linear regression models with missing observations on the response variable, presenting two estimators of regression...

Technical Information

  • File Format: PDF
  • File Size: 194 KB
  • Pages: 12
  • Language: EN
  • Author: Toutenburg; Shalabh
  • Total Downloads: 131
  • Last Updated: 1 week ago

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This PDF document about Minimum Risk Regression provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Minimum Risk Regression.

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