Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...

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, ...

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Transformation from a parametric family can improve the performance of kernel density estimation. In this article we give two data-driven estimators for the optimal transformation parameter. We ...

In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive ...

We consider density estimation when the variable of interest is subject to heteroscedastic measurement error. The density is assumed to have a smooth but unknown ...

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 ...