Single Functional Index Quantile Regression for Functional Data with Missing Data at Random
The primary goal of this research was to estimate the quantile of a conditional distribution using a semi-parametric approach in the presence of randomly missing data, where the predictor variable belongs to a semi-metric space. The authors assumed a single index structure to link the explanatory an...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
2023-02-01
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| Series: | Ekonometria |
| Subjects: | |
| Online Access: | https://journals.ue.wroc.pl/eada/article/view/1225 |
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| Summary: | The primary goal of this research was to estimate the quantile of a conditional distribution using a semi-parametric approach in the presence of randomly missing data, where the predictor variable belongs to a semi-metric space. The authors assumed a single index structure to link the explanatory and response variable. First, a kernel estimator was proposed for the conditional distribution function, assuming that the data were selected from a stationary process with missing data at random (MAR). By imposing certain general conditions, the study established the model’s uniform almost complete consistencies with convergence rates. |
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| ISSN: | 2449-9994 |