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Averaged Shifted Histogram (ASH) estimator at a single point

Source: R/ASH.R
ASH.Rd

Computes the ASH density estimate at point x.

Usage

ASH(
  x,
  data,
  b = compute_bi_optim(data, m = rep(1, ncol(data))),
  m = rep(1, ncol(data)),
  min_vals = apply(data, 2, min),
  max_vals = apply(data, 2, max)
)

# S3 method for class 'ASH'
print(x, ...)

Arguments

x

Object of class "ASH".

data

Numeric matrix or data frame of observations (n x d).

b

Positive numeric vector of bandwidths (length d).

m

Positive integer vector of shifts (length d).

min_vals

Numeric vector of lower grid bounds (length d).

max_vals

Numeric vector of upper grid bounds (length d).

...

Additional arguments (unused).

Value

A list with class c("glbfp_fit", "ASH") containing: x, estimation, b, m, method, and dimension.

Details

m controls the number of shifted histograms used in each dimension. Missing and non-finite values are not accepted; remove or impute them before calling the estimator.

Methods (by generic)

  • print(ASH): Print method for object of class "ASH".

References

Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley. doi:10.1002/9780470316849.

See also

ASH_estimate(), LBFP(), GLBFP(), compute_bi_optim()

Examples

x <- c(200, 30)
b <- c(0.5, 0.5)
m <- c(1, 1)
ASH(x, ashua[, -3], b = b, m = m)
#> ASH Density Estimation:
#> Point: (200, 30) 
#> Estimated density: 0.00820232 
#> Bandwidths (b): 0.5, 0.5 
#> Shifts (m): 1, 1