@article{article_796694, title={The $k$ nearest neighbors local linear estimator of functional conditional density when there are missing data}, journal={Hacettepe Journal of Mathematics and Statistics}, volume={51}, pages={914–931}, year={2022}, DOI={10.15672/hujms.796694}, author={Almanjahie, İbrahim and Mesfer, Wafaa and Ali, Laksaci}, keywords={functional data analysis, small ball probability, local linear method, k nearest neighbors, conditional density, almost complete convergence}, abstract={Our key aim is to propose effective estimators for the conditional probability density of a scalar response variable given a functional co-variable, where the response variable is considered to have missing data at random. Such estimators are constructed by combining the approaches of the local linear method and the kernel nearest neighborhood. The main feature of this estimation is the possibility to model the missing phenomena. Under less restrictive conditions, we show the strong consistency of the proposed estimators. To assess the efficacy of the developed estimators, empirical analysis as well as real data analyses are performed.}, number={3}, publisher={Hacettepe University}, organization={King Khalid University}