kldest (version 1.0.0)

convergence_rate: Empirical convergence rate of a KL divergence estimator

Description

Subsampling-based confidence intervals computed by kld_ci_subsampling() require the convergence rate of the KL divergence estimator as an input. The default rate of 0.5 assumes that the variance term dominates the bias term. For high-dimensional problems, depending on the data, the convergence rate might be lower. This function allows to empirically derive the convergence rate.

Usage

convergence_rate(
  estimator,
  X,
  Y = NULL,
  q = NULL,
  n.sizes = 4,
  spacing.factor = 1.5,
  typical.subsample = function(n) sqrt(n),
  B = 500L,
  plot = FALSE
)

Value

A scalar, the parameter \(\beta\) in the empirical convergence rate \(n^-\beta\) of the estimator to the true KL divergence. It can be used in the convergence.rate argument of kld_ci_subsampling()

as convergence.rate = function(n) n^beta.

Arguments

estimator

A KL divergence estimator.

X, Y

n-by-d and m-by-d data frames or matrices (multivariate samples), or numeric/character vectors (univariate samples, i.e. d = 1), representing n samples from the true distribution \(P\) and m samples from the approximate distribution \(Q\) in d dimensions. Y can be left blank if q is specified (see below).

q

The density function of the approximate distribution \(Q\). Either Y or q must be specified. If the distributions are all continuous or all discrete, q can be directly specified as the probability density/mass function. However, for mixed continuous/discrete distributions, q must be given in decomposed form, \(q(y_c,y_d)=q_{c|d}(y_c|y_d)q_d(y_d)\), specified as a named list with field cond for the conditional density \(q_{c|d}(y_c|y_d)\) (a function that expects two arguments y_c and y_d) and disc for the discrete marginal density \(q_d(y_d)\) (a function that expects one argument y_d). If such a decomposition is not available, it may be preferable to instead simulate a large sample from \(Q\) and use the two-sample syntax.

n.sizes

Number of different subsample sizes to use (default: 4).

spacing.factor

Multiplicative factor controlling the spacing of sample sizes (default: 1.5).

typical.subsample

A function that produces a typical subsample size, used as the geometric mean of subsample sizes (default: sqrt(n)).

B

Number of subsamples to draw per subsample size.

plot

A boolean (default: FALSE) controlling whether to produce a diagnostic plot visualizing the fit.

Details

References:

Politis, Romano and Wolf, "Subsampling", Chapter 8 (1999), for theory.

The implementation has been adapted from lecture notes by C. J. Geyer, https://www.stat.umn.edu/geyer/5601/notes/sub.pdf

Examples

    # NN method usually has a convergence rate around 0.5:
    set.seed(0)
    convergence_rate(kld_est_nn, X = rnorm(1000), Y = rnorm(1000, mean = 1, sd = 2))