kernelvektning
Kernelvektning, or kernel weighting, is a method in statistics and data analysis for assigning weights to observations based on their distance from a target point, using a kernel function. The weighted data are then used to estimate local properties such as a mean, regression function, or probability density.
In practice, weights are computed as w_i = K((x - x_i)/h), possibly normalized so that the weights sum
Common kernel functions include the Gaussian, Epanechnikov, uniform (rectangular), triangular, and biweight kernels. While the exact
Applications of kernel weighting include kernel density estimation, where weights estimate a density function at a
Bandwidth selection is a central practical task, addressed by rules of thumb, cross-validation, plug-in methods, or