Asymptotic normality for a local composite quantile regression estimator of regression function with truncated data
| Autor: | Hui-Zeng Zhang, Weimin Ma, Li-Min Wen, Jiang-Feng Wang |
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| Rok vydání: | 2013 |
| Předmět: |
Statistics and Probability
Statistics::Theory Mean squared error Trimmed estimator Newey–West estimator Statistics::Computation Efficient estimator Minimum-variance unbiased estimator Bias of an estimator Statistics Consistent estimator Statistics::Methodology Statistics Probability and Uncertainty Invariant estimator Mathematics |
| Zdroj: | Statistics & Probability Letters. 83:1571-1579 |
| ISSN: | 0167-7152 |
| DOI: | 10.1016/j.spl.2013.02.022 |
| Popis: | In this paper, we construct a local linear composite quantile regression (CQR) estimator of regression function for left-truncated data, which extends the CQR method to the left-truncated model. The asymptotic normality of the proposed estimator is also established. The estimator is much more efficient than the local linear regression estimator for commonly-used non-normal error distributions via simulations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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