Semi-parametric estimation of shifts
By Fabrice Gamboa, Jean-Michel Loubes, Elie Maza. 2007
72 citations recorded by the source corpus.
We observe a large number of functions differing from each other only by a translation parameter. While the main pattern is unknown, we propose to estimate the shift parameters using $M$-estimators. Fourier transform enables to transform this statistical problem into a semi-parametric framework. We study the convergence of the estimator and provide its asymptotic behavior. Moreover, we use the method in the applied case of velocity curve forecasting.
Subfields: Applied Mathematics, Ocean Engineering, Oceanography
View Paper: https://www.semanticscholar.org/paper/a01feaa1522f9e0f5db5624ddcdf3df7e9d56668
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Thibaut Le Gouic
- Jean-Michel Loubes
- Thierry Klein
- Jérémie Bigot
- Victor M. Panaretos
- Yoav Zemel
- Vladimir Spokoiny
- Alexandra L. Suvorikova
Citations in the corpus
- Characterization of barycenters in the Wasserstein space by averaging optimal transport maps (2012, unknown): “[4, 11, 12, 13, 14, 24, 44] and the review proposed in [5].”
- Existence and consistency of Wasserstein barycenters (2015, background): “This problem arises naturally for awide range of statistical research fields such as functional data analysis for instance in [7,18,25] and references therein, image analysis in [29] or [5], shape analysis in [20] or [19] with many applications ranging from biology in [12] to pattern recognition [26”
- Integral approximation by kernel smoothing (2014, unknown): “3), or location parameter estimation (see [10]).”
- Consistent estimation of a population barycenter in the Wasserstein space (2013, background): “[7, 10, 8, 16, 37], since it represents a benchmark for the statistical analysis of deformable models.”