Boosting for real and functional samples: an application to an environmental problem

DOI10.1007/s00477-007-0156-8zbMath1172.62074OpenAlexW2001109459MaRDI QIDQ839450

B. M. Fernández de Castro, Wenceslao González Manteiga

Publication date: 2 September 2009

Published in: Stochastic Environmental Research and Risk Assessment (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00477-007-0156-8

zbMATH Keywords

neural networksboostingfunctional dataair pollutants

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Ecology (92D40) Applications of functional analysis in probability theory and statistics (46N30) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (1)

Spatial mode estimation for functional random fields with application to bioturbation problem

Uses Software

• fda (R)
• ElemStatLearn

Cites Work

• Unnamed Item
• Applied functional data analysis. Methods and case studies
• Rates of convergence of autocorrelation estimates for autoregressive Hilbertian processes
• Autoregressive Forecasting of Some Functional Climatic Variations
• Approximation spline de la prevision d'un processus fonctionnel autorégressif d'ordre 1
• Boosting With theL₂Loss
• A family of minimax rates for density estimators in continuous time
• Doubly stochastic Hilbertian processes
• Predicting Using Box-Jenkins, Nonparametric, and Bootstrap Techniques
• The elements of statistical learning. Data mining, inference, and prediction
• Improving nonparametric regression methods by bagging and boosting.

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