Adaptive non-asymptotic confidence balls in density estimation
ESAIM: Probability and Statistics (2012)
- Volume: 16, page 61-85
- ISSN: 1292-8100
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topLerasle, Matthieu. "Adaptive non-asymptotic confidence balls in density estimation." ESAIM: Probability and Statistics 16 (2012): 61-85. <http://eudml.org/doc/222474>.
@article{Lerasle2012,
abstract = {We build confidence balls for the common density s of a real valued sample X1,...,Xn. We use resampling methods to estimate the projection of s onto finite dimensional linear spaces and a model selection procedure to choose an optimal approximation space. The covering property is ensured for all n ≥ 2 and the balls are adaptive over a collection of linear spaces.},
author = {Lerasle, Matthieu},
journal = {ESAIM: Probability and Statistics},
keywords = {Confidence balls; density estimation; resampling methods; confidence balls},
language = {eng},
month = {7},
pages = {61-85},
publisher = {EDP Sciences},
title = {Adaptive non-asymptotic confidence balls in density estimation},
url = {http://eudml.org/doc/222474},
volume = {16},
year = {2012},
}
TY - JOUR
AU - Lerasle, Matthieu
TI - Adaptive non-asymptotic confidence balls in density estimation
JO - ESAIM: Probability and Statistics
DA - 2012/7//
PB - EDP Sciences
VL - 16
SP - 61
EP - 85
AB - We build confidence balls for the common density s of a real valued sample X1,...,Xn. We use resampling methods to estimate the projection of s onto finite dimensional linear spaces and a model selection procedure to choose an optimal approximation space. The covering property is ensured for all n ≥ 2 and the balls are adaptive over a collection of linear spaces.
LA - eng
KW - Confidence balls; density estimation; resampling methods; confidence balls
UR - http://eudml.org/doc/222474
ER -
References
top- S. Arlot, Model selection by resampling penalization. Electron. J. Statist.3 (2009) 557–624.
- S. Arlot and P. Massart, Data-driven calibration of penalties for least-squares regression. J. Mach. Learn. Res.10 (2009) 245–279.
- S. Arlot, G. Blanchard and E. Roquain, Resampling-based confidence regions and multiple tests for a correlated random vector, in Learning theory. Lect. Notes Comput. Sci.4539 (2007) 127–141.
- Y. Baraud, Confidence balls in Gaussian regression. Ann. Statist.32 (2004) 528–551.
- R. Beran, REACT scatterplot smoothers : superefficiency through basis economy. J. Amer. Statist. Assoc.95 (2000) 155–171.
- R. Beran and L. Dümbgen, Modulation of estimators and confidence sets. Ann. Statist.26 (1998) 1826–1856.
- L. Birgé and P. Massart, From model selection to adaptive estimation, in Festschrift for Lucien Le Cam. Springer, New York (1997) 55–87.
- L. Birgé and P. Massart, Minimal penalties for Gaussian model selection. Probab. Theory Relat. Fields138 (2007) 33–73.
- T. Cai and M.G. Low, Adaptive confidence balls. Ann. Statist.34 (2006) 202–228.
- B. Efron, Bootstrap methods : another look at the jackknife. Ann. Statist.7 (1979) 1–26.
- M. Fromont and B. Laurent, Adaptive goodness-of-fit tests in a density model. Ann. Statist.34 (2006) 680–720.
- C.R. Genovese and L. Wasserman, Confidence sets for nonparametric wavelet regression. Ann. Statist.33 (2005) 698–729.
- C. Genovese and L. Wasserman, Adaptive confidence bands. Ann. Statist.36 (2008) 875–905.
- M. Hoffmann and O. Lepski, Random rates in anisotropic regression. Ann. Statist.30 (2002) 325–396. With discussions and a rejoinder by the authors.
- C. Houdré and P. Reynaud-Bouret, Exponential inequalities, with constants, for U-statistics of order two, in Stochastic inequalities and applications. Progr. Probab.56 (2003) 55–69.
- Y.I. Ingster, Asymptotically minimax hypothesis testing for nonparametric alternatives. I. Math. Methods Stat.2 (1993) 85–114.
- Y.I. Ingster, Asymptotically minimax hypothesis testing for nonparametric alternatives. II. Math. Methods Stat.2 (1993) 171–189.
- Y.I. Ingster, Asymptotically minimax hypothesis testing for nonparametric alternatives. III. Math. Methods Stat.2 (1993) 249–268.
- A. Juditsky and S. Lambert-Lacroix, Nonparametric confidence set estimation. Math. Methods Stat.12 (2003) 410–428.
- A. Juditsky and O. Lepski, Evaluation of the accuracy of nonparametric estimators. Math. Methods Stat.10 (2001) 422–445. Meeting on Mathematical Statistics, Marseille (2000).
- B. Laurent, Estimation of integral functionnals of a density. Ann. Statist.24 (1996) 659–681.
- B. Laurent, Adaptive estimation of a quadratic functional of a density by model selection. ESAIM : PS9 (2005) 1–18 (electronic).
- O.V. Lepski, How to improve the accuracy of estimation. Math. Methods Stat.8 (1999) 441–486.
- M. Lerasle, Optimal model selection in density estimation. Preprint (2009).
- K.C. Li, Honest confidence regions for nonparametric regression. Ann. Statist.17 (1989) 1001–1008.
- M.G. Low, On nonparametric confidence intervals. Ann. Statist.25 (1997) 2547–2554.
- P. Massart, Concentration inequalities and model selection. Springer, Berlin. Lect. Notes Math.1896 (2007). Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour (2003). With a foreword by Jean Picard.
- J. Robins and A. van der Vaart, Adaptive nonparametric confidence sets. Ann. Statist.34 (2006) 229–253.
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