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Updated: May 8, 2026
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Doubly robust and efficient estimators for heteroscedastic partially linear single-index models allowing high
1Texas A&M University, College Station, USA.
Journal of the Royal Statistical Society. Series B, Statistical Methodology
|August 24, 2013
Summary
We developed new statistical methods for analyzing complex data, improving parameter estimation in heteroscedastic partially linear single-index models. Our approach offers robust and efficient estimation, even with misspecified components.
Area of Science:
- Statistics
- Econometrics
Background:
- High-dimensional data presents challenges for statistical modeling.
- Existing methods for heteroscedastic partially linear single-index models often require strict assumptions.
Purpose of the Study:
- To develop robust and consistent estimators for heteroscedastic partially linear single-index models.
- To investigate the necessity of linearity assumptions and the impact of non-parametric component estimation.
Main Methods:
- Proposed a class of consistent estimators using a novel weighting strategy.
- Investigated theoretical properties and conducted numerical simulations.
- Applied methods to a gender discrimination dataset.
Main Results:
- Demonstrated that linearity assumptions are not essential for consistent estimation.
- Developed estimators robust to misspecification of the non-parametric component.
- Identified an efficient estimator within the proposed family.
Conclusions:
- The proposed weighting strategy provides robust and consistent estimation for complex models.
- The new methods offer improved performance and flexibility compared to traditional approaches.
- The findings have practical implications for analyzing real-world data, such as in gender discrimination studies.
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