R/CVbw.R

In kerdiest: Nonparametric Kernel Estimation of Distribution Function

CVbw <- function(type_kernel = "n", vec_data, n_pts = 100, seq_bws = NULL) {
  prob_quantile <- 0
  ss <- quantile(vec_data, c(prob_quantile, 1 - prob_quantile))
  y <- seq(ss[1], ss[2], length.out = n_pts)
  if (is.null(seq_bws)) {
    seq_bws <- seq((max(vec_data) - min(vec_data)) / 200, (max(vec_data) - min(vec_data)) / 2, length.out = 50)
  }
  n_bws <- length(seq_bws)
  CVfunction <- numeric(length = n_bws)
  for (i in 1:n_bws) {
    integrand <- apply(((outer(y, vec_data, "-") >= 0) - t(kernel_distribution_without_i(type_kernel, y, vec_data, seq_bws[i])))^2, 1, mean)
    CVfunction[i] <- simp_int(y, integrand)$value
  }
  i0 <- which.min(CVfunction)
  CVbw_val <- seq_bws[i0]
  list(seq_bws = seq_bws, CVfunction = CVfunction, bw = CVbw_val)
}