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Estimation in regression models with externally estimated parameters.

R Todd Ogden1, Thaddeus Tarpey

  • 1Department of Biostatistics, Columbia University, 6th floor, 722 West 168th Street, New York, NY 10032, USA. to166@columbia.edu

Biostatistics (Oxford, England)
|July 16, 2005
PubMed
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This study introduces new methods to accurately calculate standard errors for regression model parameters when data comes from multiple sources. These techniques, including asymptotic and bootstrap approaches, account for all sources of variability in parameter estimation.

Area of Science:

  • Statistics
  • Mathematical Modeling

Background:

  • Regression models often use parameters estimated from separate data sources.
  • This approach is common in compartment modeling with external input functions.

Purpose of the Study:

  • To develop and present methods for calculating standard errors that account for all sources of variability.
  • To improve the accuracy of parameter estimation in regression models using external data.

Main Methods:

  • Asymptotic approaches for standard error calculation.
  • Bootstrap-based methods for assessing variability.
  • Integration of estimates from separate data sources into primary regression models.

Main Results:

  • The proposed methods provide accurate standard errors for estimated regression parameters.

Related Experiment Videos

  • Simulations demonstrate the effectiveness of the asymptotic and bootstrap approaches.
  • Examples illustrate the practical application of these techniques.

    • Conclusions:

    • The presented methods effectively address the challenge of multi-source data in regression.
    • Accurate standard error computation is crucial for reliable parameter estimation in complex models.