Local linear quantile regression with truncated and dependent data
| Autor: | Weimin Ma, Li-Min Wen, Jiang-Feng Wang, Guo-Liang Fan |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Statistics::Theory Sequence Estimator Asymptotic distribution Quantile regression Nonparametric regression Mixing (mathematics) Bayesian multivariate linear regression Statistics Statistics::Methodology Applied mathematics Statistics Probability and Uncertainty Mathematics Quantile |
| Zdroj: | Statistics & Probability Letters. 96:232-240 |
| ISSN: | 0167-7152 |
| DOI: | 10.1016/j.spl.2014.09.029 |
| Popis: | In this paper, we construct a nonparametric regression quantile estimator by using the local linear fitting for left-truncated data, and establish the Bahadur-type representation and asymptotic normality of the proposed estimator when the observations form a stationary α -mixing sequence. Finite-sample performance of the estimator is investigated via simulation studies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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