The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...
Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...
We propose a nonparametric estimation theory for the occupation density, the drift vector, and the diffusion matrix of multivariate diffusion processes. The estimators are sample analogues to ...
The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 16, No. 4 (Dec., 1988), pp. 399-409 (11 pages) Recursive estimates fn (r)(x) of the rth derivative f(r)(x)(r=0,1) of the ...
Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...
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