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Minimax Risk: Pinsker Bound
COMMUNICATION THEORY, STATISTICAL DENSITY ESTIMATION FISHER INFORMATION KERNEL ESTIMATORS LINEAR ESTIMATORS, BAYES LOCAL ASYMPTOTIC NORMALITY METHOD OF SIEVES MINIMAX ESTIMATION NOISE (SIGNAL PROCESSING IN THE PRESENCE OF ) PREDICTION AND FILTERING LINEAR SIEVES, METHOD OF SPECTRAL ANALYSIS SHRINKAGE ESTIMATORS SMOOTHNESS PRIORS SOBOLEV SPACES SPLINE FUNCTIONS STATIONARY PROCESSES STEIN EFFECT
2015/8/25
We give an account of the Pinsker bound describing the exact asymptotics of the minimax risk in a class of nonparametric smoothing problems. The parameter spaces are Sobolev classes or ellipsoids, and...
Consider estimating the mean vector from data Nn(; 2I ) with lq norm loss,
q 1, when is known to lie in an n-dimensional lp ball, p 2 (0; 1). For large
n, the ratio of minimax linear risk to...
Asymptotic minimax risk of predictive density estimation for non-parametric regression
asymptotic minimax risk convergence rate non-parametric regression
2010/10/19
We consider the problem of estimating the predictive density of future observations from a non-parametric regression model. The density estimators are evaluated under Kullback--Leibler divergence and ...
Lower bounds for the minimax risk using $f$-divergences and applications
Minimax lower bounds f-divergences Fano’sinequality Pinsker’s inequality Reconstruction from supportfunctions
2010/3/10
A new lower bound involving f-divergences between
the underlying probability measures is proved for the minimax
risk in estimation problems. The proof just uses the convexity
of the function f and ...
Non-asymptotic minimax risk for Hellinger balls
Non-asymptotic minimax risk Hellinger balls
2009/9/24
Non-asymptotic minimax risk for Hellinger balls。