R: Quantile Regression Fitting via Interior Point Methods

rq.fit.pfnb {quantreg} | R Documentation

## Quantile Regression Fitting via Interior Point Methods

### Description

This is a lower level routine called by `rq()` to compute quantile regression parameters using the Frisch-Newton algorithm. It uses a form of preprocessing to accelerate the computations for situations in which several taus are required for the same model specification.

### Usage

    rq.fit.pfnb(x, y, tau, m0 = NULL, eps = 1e-06)

### Arguments

`x` | The design matrix
`y` | The response vector
`tau` | The quantiles of interest, must lie in (0,1), be sorted and preferably equally spaced.
`m0` | An initial reduced sample size by default is set to be `round((n * (log(p) + 1) )^(2/3)` this could be explored further to aid performance in extreme cases.
`eps` | A tolerance parameter intended to bound the confidence band entries away from zero.

### Details

The details of the Frisch-Newton algorithm are explained in Koenker and Portnoy (1997), as is the preprocessing idea which is related to partial sorting and the algorithms such as `kuantile` for univariate quantiles that operate in time O(n). The preprocessing idea of exploiting nearby quantile solutions to accelerate estimation of adjacent quantiles is proposed in Chernozhukov et al (2020). This version calls a fortran version of the preprocessing algorithm that accepts multiple taus. The preprocessing approach is also implemented for a single tau in `rq.fit.pfn` which may be regarded as a prototype for this function since it is written entirely in R and therefore is easier to experiment with.

### Value

returns a list with elements consisting of

1. coefficientsa matrix of dimension ncol(x) by length(taus)

2. nit a 5 by m matrix of iteration counts: first two coordinates of each column are the number of interior point iterations, the third is the number of observations in the final globbed sample size, and the last two are the number of fixups and bad-fixups respectively. This is intended to aid fine tuning of the initial sample size, m0.

3. info an m-vector of convergence flags

### References

Koenker, R. and S. Portnoy (1997). The Gaussian Hare and the Laplacian Tortoise: Computability of squared-error vs. absolute-error estimators, with discussion, Statistical Science, 12, 279-300.

Chernozhukov, V., I., Fernandez-Val, and Melly, B. (2020), 'Fast algorithms for the quantile regression process', Empirical Economics, forthcoming.

### See Also

`rq`, `rq.fit.br`, `rq.fit.pfn`

[Package quantreg version 5.83 Index]