@misc{Hamri_Mohamed_Mehdi_Single_2022, author={Hamri, Mohamed Mehdi and Mekki, Sanaà Dounya and Rabhi, Abbes and Kadiri, Nadia}, identifier={DOI: 10.15611/eada.2022.1.03}, year={2022}, rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy}, publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu}, description={Econometrics = Ekonometria, 2022, Vol. 26, No. 1, s. 31-62}, language={eng}, abstract={The main objective of this paper was to estimate non-parametrically the quantiles of a conditional distribution based on the single-index model in the censorship model when the sample is considered as independent and identically distributed (i.i.d.) random variables. First of all, a kernel type estimator for the conditional cumulative distribution function (cond-cdf) is introduced. Then the paper gives an estimation of the quantiles by inverting this estimated cond-cdf, the asymptotic properties are stated when the observations are linked with a single-index structure. Finally, a simulation study was carried out to evaluate the performance of this estimate.}, title={Single functional index quantile regression for independent functional data under right-censoring}, type={artykuł}, keywords={censored data, functional data, kernel estimation, normality, non-parametric estimation, small ball probability, dane cenzurowane, estymator jądrowy, normalność, estymacja nieparametryczna, prawdopodobieństwo small ball}, }