Concentration Inequalities and Confidence Bands for Needlet Density Estimators on Compact Homogeneous Manifolds

Abstract

Let $X_1,...,X_n$ be a random sample from some unknown probability density $f$ defined on a compact homogeneous manifold $\mathbf{M}$ of dimension $d\ge 1$. Consider a 'needlet frame' $\{{\varphi }_{j\eta }\}$ describing a localised projection onto the space of eigenfunctions of the Laplace operator on $\mathbf{M}$ with corresponding eigenvalues less than $2^{2j}$, as constructed in \cite{GP10}. We prove non-asymptotic concentration inequalities for the uniform deviations of the linear needlet density estimator $f_n(j)$ obtained from an empirical estimate of the needlet projection ${\sum }_{\eta }{\varphi }_{j\eta }\int f{\varphi }_{j\eta }$ of $f$. We apply these results to construct risk-adaptive estimators and nonasymptotic confidence bands for the unknown density $f$. The confidence bands are adaptive over classes of differentiable and H\"{older}-continuous functions on $\mathbf{M}$ that attain their Hölder exponents.

Probability Theory and Related Fields, to appear

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• Multiresolution analysis and adaptive estimation on a sphere using stereographic wavelets