Boundary bias refers to the systematic error that occurs in kernel density estimation when data points are near the boundaries of the support of the distribution. This bias arises because the kernel functions used to estimate the density may not adequately account for the limited available data at the boundaries, leading to underestimation or overestimation of the density in those regions. Understanding boundary bias is crucial for accurate statistical modeling and inference, especially when dealing with data that is confined within specific limits.
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