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 ...
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 ...
The main uniform convergence results of Hansen (2008, Econometric Theory 24, 726-748) are generalized in two directions: Data are allowed to (a) be heterogeneously dependent and (b) depend on a ...
Kernel density estimates, as commonly applied, generally have no exact model-based interpretation since they violate conditions that define coherent joint distributions. The issue of marginalization ...
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