qboost: Quantile Boosting for high-dimensional data

In SvenKlaassen/qboost: Quantile Boosting for High-Dimensions

Quantile Boosting for high-dimensional data

Description

Applying a greedy algorithm to minimize the expected smoothed check loss. Employs the Weak Restricted Greedy Algorithm. For the orthogonal variant, set stepsize to NULL.

Usage

qboost(
  X,
  Y,
  tau = 0.5,
  m_stop = 1,
  h = 0.1,
  kernel = "Gaussian",
  stepsize = NULL
)

Arguments

X	(matrix) A matrix of covariates.
Y	(numeric()) A vector of responses.
tau	(numeric(1L)) A quantile.
m_stop	(integer(1L)) The number of boosting steps.
h	(numeric(1L)) The bandwith for smoothing.
kernel	(character(1L)) The kernel for smoothing.
stepsize	(numeric(1L)) The stepsize of the boosting procedure. Set to NULL for WCGA.

Value

An object with S3 class qboost

• coeff_path The cofficients along the boosting steps as a matrix (starts with a zero vector).

• selection_path The selected covariates as a vector.

• params Additional parameters.

See Also

predict.qboost

SvenKlaassen/qboost documentation built on Nov. 8, 2022, 12:13 p.m.