Kazashi, YoshihitoNobile, Fabio2023-06-192023-06-192023-01-0110.1137/22M147476Xhttps://infoscience.epfl.ch/handle/20.500.14299/198297WOS:000996502200018arXiv:2108.12699A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined by a linear equation. An error analysis for the mean integrated squared error is established in a general reproducing kernel Hilbert space setting. The theory developed is then applied to estimate probability density func-tions belonging to weighted Korobov spaces, for which a dimension-independent convergence rate is established. Under a suitable smoothness assumption, our method attains a rate arbitrarily close to the optimal rate. Numerical results support our theory.Mathematics, AppliedMathematicsdensity estimationhigh-dimensional approximationkernel methodstext::journal::journal article::research article