post_processed_grad_descent: Smoothed Quantile Regression with Post-Processing

In be-green/fqr: Fast Quantile Regression

Description

Smoothed Quantile Regression with Post-Processing

Usage

post_processed_grad_descent(
  X,
  y,
  tau,
  lambda = 0,
  nwarmup_samples = 0.1 * nrow(X),
  lp_size = 10000
)

Arguments

X	design matrix
y	outcome variable
tau	target quantile
lambda	optional weight on penalty function
nwarmup_samples	number of samples to use for warmup in approximat quantile regression
lp_size	size of linear programming problem passed to the simplex algorithm

Details

This function performs smoothed quantile regression w/ post-processing to ensure accuracy of the approximate first-order method.

be-green/fqr documentation built on Dec. 19, 2021, 7:41 a.m.