Improved kernel density estimation for clustered data using regularisation and deconvolution
Q. Chen, David J. Sandoz, R.J. Wynne, Uwe Krüger · 2000
To extract multivariate probability density functions (PDF) from a clustered training data set for condition monitoring purposes, a modified kernel density estimation method is suggested using regularisation and deconvolution techniques. Case studies show that it is a useful pragmatic method for real industrial data.