Robust nonparametric estimation of the intensity function of point data
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Publication:2006835
DOI10.1007/s10182-008-0065-2zbMath1477.62266OpenAlexW2064764368MaRDI QIDQ2006835
Publication date: 12 October 2020
Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10182-008-0065-2
kernel regressioncross-validationrecursive algorithmslocal polynomialearthquake dataM-type estimationSan Francisco bay
Inference from spatial processes (62M30) Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Nonparametric robustness (62G35) Seismology (including tsunami modeling), earthquakes (86A15)
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Cites Work
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- Adaptive \(M\)-estimation in nonparametric regression
- On automatic boundary corrections
- Non-parametric smoothing of spatio-temporal point processes
- Scale space view of curve estimation.
- Smoothing methods in statistics
- Cross-validation in nonparametric regression with outliers
- On consistency of redescending M-kernel smoothers
- Bandwidth selection in robust smoothing
- Robust Locally Weighted Regression and Smoothing Scatterplots
- Edge-Preserving Smoothers for Image Processing
- The L 1 Method for Robust Nonparametric Regression
- Stochastic Declustering of Space-Time Earthquake Occurrences
- Nonparametric regression for nonstationary processes
- On the estimation of poles in intensity functions
- Automatic bandwidth selection in robust nonparametric regression
- SiZer for Exploration of Structures in Curves
- CONSISTENCY OF ASYMMETRIC KERNEL DENSITY ESTIMATORS AND SMOOTHED HISTOGRAMS WITH APPLICATION TO INCOME DATA
- An Introduction to the Theory of Point Processes
- M-Smoother with local Linear Fit
- Robust Statistics
