High-dimensional statistical inference encompasses methods for drawing reliable conclusions when the number of variables rivals or exceeds the sample size. Such settings occur routinely in genomics, ...
Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...
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