1. Kernel density estimation for multiplicative distortion measurement regression models.
- Author
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Zhang, Jun, Chen, Aixian, and Wei, Zhenghong
- Subjects
- *
PROBABILITY density function , *REGRESSION analysis , *STATISTICAL hypothesis testing , *NONLINEAR regression , *MEASUREMENT errors , *NULL hypothesis - Abstract
We propose a kernel density based estimation for nonlinear regression models when neither the response variable nor the covariates can be directly observed, but are measured with multiplicative distortion measurement errors. For the hypothesis testing of parametric components, restricted estimators under the null hypothesis and test statistics are proposed. The asymptotic properties for the estimators and test statistics are established. A bootstrap procedure is proposed to calculate critical values. Lastly, we make some comparisons between the proposed estimator and nonlinear least squares estimator through the simulation. Our simulation studies indicate that the kernel density based estimator provides a better alternative under some settings, particularly for multimodal distribution of the error term. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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