step_fmx: Forward Selection of gh-parsimonious Model with Fixed Number...

In QuantileGH: Quantile Least Mahalanobis Distance Estimator for Tukey g-&-h Mixture

Forward Selection of gh-parsimonious Model with Fixed Number of Components K

Description

To select the gh-parsimonious mixture model, i.e., with some g and/or h parameters equal to zero, conditionally on a fixed number of components K.

Usage

step_fmx(
  object,
  test = c("BIC", "AIC"),
  direction = c("forward", "backward"),
  ...
)

Arguments

object	fmx object
test	character scalar, criterion to be used, either Akaike's information criterion AIC-like, or Bayesian information criterion BIC-like (default).
direction	character scalar, 'forward' (default) or 'backward'
...	additional parameters, currently not in use

Details

The algorithm starts with quantile least Mahalanobis distance estimates of either the full mixture of Tukey g-&-h distributions model, or a constrained model (i.e., some g and/or h parameters equal to zero according to the user input). Next, each of the non-zero g and/or h parameters is tested using the likelihood ratio test. If all tested g and/or h parameters are significantly different from zero at the level 0.05 the algorithm is stopped and the initial model is considered gh-parsimonious. Otherwise, the g or h parameter with the largest p-value is constrained to zero for the next iteration of the algorithm.

The algorithm iterates until only significantly-different-from-zero g and h parameters are retained, which corresponds to gh-parsimonious Tukey g-&-h mixture model.

Value

Function step_fmx() returns an object of S3 class 'step_fmx', which is a list of selected models (in reversed order) with attribute(s) 'direction' and 'test'.

See Also

step

QuantileGH documentation built on April 3, 2025, 8:35 p.m.