Non-parametric kernel regression for multinomial data

DOI10.1016/j.jmva.2005.12.008zbMath1101.62032OpenAlexW1972906218MaRDI QIDQ855913

Hidenori Okumura, Kanta Naito

Publication date: 7 December 2006

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2005.12.008

zbMATH Keywords

kernel smoothingmultinomial datapower-divergence measure

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20)

Related Items (2)

Non-parametric kernel regression for multinomial data ⋮ Mixed spline smoothing and kernel estimator in biresponse nonparametric regression

Uses Software

• KernSmooth

Cites Work

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• Non-parametric kernel regression for multinomial data
• Bandwidth selection for local polynomial smoothing of multinomial data
• Semiparametric density estimation by local \(L_ 2\)-fitting.
• Smoothing methods in statistics
• A comparison of related density-based minimum divergence estimators
• An Effective Bandwidth Selector for Local Least Squares Regression
• Analysis of Categorical Data by Linear Models
• Kernel and Probit Estimates in Quantal Bioassay
• Robust and efficient estimation by minimising a density power divergence
• Smoothing sparse multinomial data using local polynomial fitting
• Weighted kernel estimators in nonparametric binomial regression
• On a certain class of nonparametric density estimators with reduced bias

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