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Estimation via corrected scores in general semiparametric regression models with error-prone covariates
Arnab Maity1, Tatiyana V Apanasovich
1Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A. amaity@ncsu.edu.
Electronic Journal of Statistics
|July 10, 2012
Summary
This study introduces a new method for regression analysis with measurement error in covariates. The approach simplifies estimation in semiparametric models, offering a practical solution for complex data.
Area of Science:
- Statistics
- Econometrics
- Biostatistics
Background:
- Semiparametric regression models are widely used but can be complicated by measurement error in covariates.
- Traditional methods for handling measurement error often involve complex calculations, such as solving high-dimensional integral equations.
Purpose of the Study:
- To develop a computationally simple and flexible method for estimation in semiparametric regression models with error-prone covariates.
- To provide a robust approach that does not require assumptions about the distribution of mismeasured covariates.
Main Methods:
- A correction to a criterion function is applied to account for measurement error.
- The study utilizes profile kernel and backfitting estimation techniques.
- Asymptotic distribution of the proposed estimators is derived.
Main Results:
- The proposed methodology offers a simple implementation compared to computationally demanding alternatives.
- Numerical studies confirm the applicability to Poisson, logistic, and multivariate Gaussian partially linear models.
- The performance of the new methods is comparable to existing, more complex techniques.
Conclusions:
- The developed methods provide a practical and efficient way to handle measurement error in semiparametric regression.
- The approach is demonstrated to be effective on real-world data, such as the Nevada Test Site (NTS) Thyroid Disease Study.
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