Smoothing sparse multinomial data using local polynomial fitting

DOI10.1080/10485259708832717zbMath0889.62028OpenAlexW1977581830WikidataQ126249611 ScholiaQ126249611MaRDI QIDQ4365358

Ilse Augustyns, Paul Janssen, Marc Aerts

Publication date: 4 November 1997

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485259708832717

zbMATH Keywords

local polynomial smootherscentral limit theorembiasoptimal rate of convergenceweighted regressionsparse multinomial datamean sum of squared errors

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05) Contingency tables (62H17)

Related Items (10)

Improving SAMC using smoothing methods: Theory and applications to Bayesian model selection problems ⋮ Cross-validation and the estimation of probability distributions with categorical data ⋮ Non-parametric kernel regression for multinomial data ⋮ On close relations of local likelihood density estimation ⋮ Nonparametric estimation of distributions with categorical and continuous data ⋮ Local Polynomial Estimation of Contingency Table Cell Probabilities ⋮ Inferring within-host bottleneck size: a Bayesian approach ⋮ Central limit theorem for the total squared error of local polynomial estimators of cell probabilities ⋮ \(L_2\)-tests for sparse multinomials ⋮ Local quasi-likelihood approach to varying-coefficient discrete-valued time series models

Cites Work

• Central limit theorem for quadratic forms for sparse tables
• Multivariate locally weighted least squares regression
• Smoothing categorical data
• A geometric combination estimator for \(d\)-dimensional ordinal sparse contingency tables
• Smoothing methods in statistics
• Smooth estimators of distribution and density functions
• On Smoothing Sparse Multinomial Data
• Robust Locally Weighted Regression and Smoothing Scatterplots

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