{'fraction_non_alphanumeric': 0.0258732212160414, 'fraction_numerical': 0.0, 'mean_word_length': 5.344262295081967, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Single Functional Index Quantile Regression for Independent Functional Data Under Right-Censoring

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.