  This paper presents an intuitive application of multivariate kernel density
estimation (KDE) for data correction. The method utilizes the expected value of
the conditional probability density function (PDF) and a credible interval to
quantify correction uncertainty. A selective KDE factor is proposed to adjust
both kernel size and shape, determined through least-squares cross-validation
(LSCV) or mean conditional squared error (MCSE) criteria. The selective
bandwidth method can be used in combination with the adaptive method to
potentially improve accuracy. Two examples, involving a hypothetical dataset
and a realistic dataset, demonstrate the efficacy of the method. The selective
bandwidth methods consistently outperform non-selective methods, while the
adaptive bandwidth methods improve results for the hypothetical dataset but not
for the realistic dataset. The MCSE criterion minimizes root mean square error
but may yield under-smoothed distributions, whereas the LSCV criterion strikes
a balance between PDF fitness and low RMSE.
