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Statistical inferences for single-index models with measurement errors.
1School of Science, Nanjing University of Science and Technology, Nanjing, People's Republic of China.
Journal of Applied Statistics
|June 16, 2022
Summary
Accurate house price prediction requires accounting for location measurement errors. This study introduces generalized likelihood ratio (GLR) tests to validate single-index models, improving real estate valuation accuracy.
Area of Science:
- Econometrics
- Statistical modeling
- Real estate valuation
Background:
- House prices are significantly influenced by location.
- Measurement errors in location data (latitude, longitude) are common.
- Existing models may not adequately address these errors.
Purpose of the Study:
- To develop and validate statistical models for house price prediction considering measurement errors in location data.
- To introduce robust statistical tests for evaluating the significance and linearity of single-index models.
Main Methods:
- Utilizing single-index models with measurement error correction.
- Employing the SIMEX (Simulation and Extrapolation) method combined with local linear estimation.
- Developing generalized likelihood ratio (GLR) tests to assess model parameters and link function linearity.
Main Results:
- The proposed SIMEX-based local linear estimators effectively handle measurement errors.
- The generalized likelihood ratio (GLR) tests provide reliable significance testing.
- Asymptotic null distributions of GLR tests are independent of nuisance parameters.
Conclusions:
- The developed GLR tests are effective for validating single-index models in the presence of location measurement errors.
- The methodology enhances the accuracy of real estate valuation models.
- The approach is validated through simulations and a real estate dataset.
Keywords:
62E2062G08SIMEXSingle-index modelgeneralized likelihood ratio testlocal linear regressionmeasurement errorsMore Related Videos
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