parametric_starts: Parametric starts

In kdensity: Kernel Density Estimation with Parametric Starts and Asymmetric Kernels

Description

A parametric start is a density function with an associated estimator which is used as a starting point in kdensity. Several parametric starts are implemented, all with maximum likelihood estimation. Custom-made parametric starts are possible, see the Structure section.

Structure

The parametric start contains three elements: The density function, an estimation function, and the support of the density. The parameters of the density function must partially match the parameters of the estimator function. The estimator function takes one argument, a numeric vector, which is passed from kdensity.

Supported parametric starts

kdensity supports more than 20 built-in starts from the univariateML package, see univariateML::univariateML_models for a list. Densities with variable support, power, are not supported. The pareto density has its support fixed to (1,Inf). The options uniform, constant makes kdensity estimate a kernel density without parametric starts.

See Also

kdensity(); kernels(); bandwidths()

Examples

start_exponential = list(
 density = stats::dexp,
 estimator = function(data) {
   c(rate = 1/mean(data))
 },
 support = c(0, Inf)
)

start_inverse_gaussian = list(
 density = extraDistr::dwald,
 estimator = function(data) {
  c(mu = mean(data),
    lambda = mean(1/data - 1/mean(data)))
  },
 support = c(0, Inf)
)

kdensity documentation built on Oct. 23, 2020, 8:32 p.m.