<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Single Functional Index Quantile Regression for Functional Data with Missing Data at Random

University Djillali Liabes of Sidi Bel Abbes

Department of Mathematics and Computer Science

Econometrics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

Single Functional Index Quantile Regression for Functional Data with Missing Data at Random

Data publikacji: 11 gru 2023

Zakres stron: 1 - 19

DOI: https://doi.org/10.15611/eada.2023.3.01

Słowa kluczowe
functional data analysis, functional single index process, kernel estimator, missing at random, nonparametric estimation, small ball probability

© 2023 Nadia Kadiri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Single Functional Index Quantile Regression for Functional Data with Missing Data at Random

Data publikacji: 11 gru 2023

Zakres stron: 1 - 19

DOI: https://doi.org/10.15611/eada.2023.3.01

Słowa kluczowefunctional data analysis, functional single index process, kernel estimator, missing at random, nonparametric estimation, small ball probability

© 2023 Nadia Kadiri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
functional data analysis, functional single index process, kernel estimator, missing at random, nonparametric estimation, small ball probability