Abstract:
We consider the problem of recovering an unknown vector from noisy data. The vector is estimated using a family of projection estimates, and the goal is finding a sufficiently good convex combination of these estimates based on the observations. We study an aggregation method for constructing estimates related to the so-called exponential weighting and present an upper bound on the mean-square risk of this method.
This publication is cited in the following 3 articles:
Werner F., “Adaptivity and Oracle Inequalities in Linear Statistical Inverse Problems: a (Numerical) Survey”, New Trends in Parameter Identification For Mathematical Models, Trends in Mathematics, eds. Hofmann B., Leitao A., Zubelli J., Birkhauser Verlag Ag, 2018, 291–316
L. Montuelle, E. Le Pennec, Springer Proceedings in Mathematics & Statistics, 250, Nonparametric Statistics, 2018, 133
Chernousova E., Golubev Yu., “Ordered Smoothers with Exponential Weighting”, Electron. J. Stat., 7 (2013), 2395–2419