kernel_density: multivariate kernel density estimator usage: dens = kernel_density(eval_points, data, bandwidth) inputs: eval_points: PxK matrix of points at which to calculate the density data: NxK matrix of data points bandwidth: positive scalar, the smoothing parameter. The fit is more smooth as the bandwidth increases. kernel (optional): string. Name of the kernel function. Default is Gaussian kernel. prewhiten bool (optional): default false. If true, rotate data using Choleski decomposition of inverse of covariance, to approximate independence after the transformation, which makes a product kernel a reasonable choice. do_cv: bool (optional). default false. If true, calculate leave-1-out density for cross validation computenodes: int (optional, default 0). Number of compute nodes for parallel evaluation debug: bool (optional, default false). show results on compute nodes if doing a parallel run outputs: dens: Px1 vector: the fitted density value at each of the P evaluation points. References: Wand, M.P. and Jones, M.C. (1995), 'Kernel smoothing'. http://www.xplore-stat.de/ebooks/scripts/spm/html/spmhtmlframe73.html
Package: econometrics