Information theoretic criteria in non-parametric density estimation. Bias and variance in the infinite dimensional case

DOI10.1016/0167-9473(91)90022-TzbMath0887.62040OpenAlexW1506269003WikidataQ58161702 ScholiaQ58161702MaRDI QIDQ2365203

Antonio Ciampi, Michał Abrahamowicz

Publication date: 25 February 1997

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-9473(91)90022-t

zbMATH Keywords

smoothing splinesvariancebiasAICBICregression splinesgraphical displaydensity estimation methodsevaluation of simulationsinfinite-dimensional estimation spacesmeta-parameters

Mathematics Subject Classification ID

Density estimation (62G07) Statistical aspects of information-theoretic topics (62B10)

Related Items (1)

Accounting for Data-Dependent Degrees of Freedom Selection When Testing the Effect of a Continuous Covariate in Generalized Additive Models

Uses Software

• IMSL Numerical Libraries

Cites Work

• Topics in statistical information theory
• Nonparametric probability density estimation by discrete maximum penalized-likelihood criteria
• Estimating the dimension of a model
• On Polya frequency functions. IV: The fundamental spline functions and their limits
• Nonparametric density estimation from censored data
• Nonparametric density estimation for censored survival data: Regression‐spline approach
• On Information and Sufficiency
• A new look at the statistical model identification
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