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Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

標本$${D=\{x_1, \cdots, x_n\}}$$が従う確率密度$${f({\bm x})}$$をカーネル関数$${K({\bm x},{\bm x}')}$$を用いて、 $${\hat f_{KDE}({\bm x ...

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 ...

This project implements and compares nonparametric estimators for convolution densities ψ = f ⋆ g, where f and g are unknown probability density functions. The implementation uses higher-order kernels ...

Under a single-index regression assumption, we introduce a new semiparametric procedure to estimate a conditional density of a censored response. The regression model can be seen as a generalization ...

The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...