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Related Concept Videos

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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Targeted maximum likelihood estimation for prediction calibration.

Jordan Brooks1, Mark J van der Laan, Alan S Go

  • 1University of California - Berkeley, CA, USA.

The International Journal of Biostatistics
|November 16, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces targeted maximum likelihood estimation (TMLE) for unbiased conditional expectation prediction. The novel TMLE procedure ensures efficient and unbiased estimation, particularly useful in complex data partitioning scenarios.

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Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Estimators of conditional expectation (prediction) typically involve a bias-variance trade-off.
  • Unbiased estimation for specific data partitions is often desirable but challenging.
  • Existing methods may not adequately address bias-variance trade-offs in partitioned data.

Purpose of the Study:

  • To develop a targeted maximum likelihood estimation (TMLE) procedure for unbiased prediction.
  • To identify conditional expectation given a partitioning as a smooth parameter.
  • To ensure updated estimators are unbiased and efficient for this smooth parameter.

Main Methods:

  • Proposed a targeted maximum likelihood estimation (TMLE) procedure.
  • Calibrated estimators with respect to data partitioning (covariate or prediction space).
  • Derived TMLE for single time-point and time-dependent predictions in a counting process framework.

Main Results:

  • The TMLE procedure updates initial estimators for unbiased and efficient estimation of the smooth parameter.
  • When partitioning is on prediction space, TMLE enforces an implicit constraint.
  • The resulting TMLE estimator equals the empirical estimator, known for unbiasedness and efficiency.

Conclusions:

  • The proposed TMLE offers a method for unbiased and efficient conditional expectation estimation.
  • This approach is applicable to both simple and complex partitioning strategies.
  • Validated TMLE for single and time-dependent predictions within a counting process model.