Nonparametric estimation of the conditional mode when the regressor is functional

DOI10.1016/j.crma.2006.11.022zbMath1104.62040OpenAlexW2085108437MaRDI QIDQ866588

Ali Laksaci, Sophie Dabo-Niang

Publication date: 14 February 2007

Published in: Comptes Rendus. Mathématique. Académie des Sciences, Paris (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.crma.2006.11.022

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Related Items (12)

Asymptotic Results of a Recursive Double Kernel Estimator of the Conditional Quantile for Functional Ergodic Data ⋮ Limiting law results for a class of conditional mode estimates for functional stationary ergodic data ⋮ Strong Consistency Rate for the Kernel Mode Estimator Under Strong Mixing Hypothesis and Left Truncation ⋮ On the strong uniform consistency of the mode estimator for censored time series ⋮ A strong linear representation for the maximum conditional hazard rate estimator in survival analysis ⋮ On the conditional density estimation for continuous time processes with values in functional spaces ⋮ Rate of uniform consistency for a class of mode regression on functional stationary ergodic data ⋮ Local linear estimation of the conditional density for functional data. ⋮ ALMOST SURE REPRESENTATIONS OF THE CONDITIONAL HAZARD FUNCTION AND ITS MAXIMUM ESTIMATION UNDER RIGHT-CENSORING AND LEFT-TRUNCATION ⋮ Note on conditional mode estimation for functional dependent data ⋮ A recursive kernel estimate of the functional modal regression under ergodic dependence condition ⋮ Rate of uniform consistency for nonparametric estimates with functional variables

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• A Note on Ergodic Processes Prediction via Estimation of the Conditional Mode Function
• A nonparametric conditional mode estimate
• Asymptotic normality of kernel estimators of the conditional mode under strong mixing hypothesis
• Dimension fractale et estimation de la régression dans des espaces vectoriels semi-normés

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