R: Preprocessing weighted bootstrap method

boot.rq.pwxy {quantreg} | R Documentation

## Preprocessing weighted bootstrap method

### Description

Bootstrap method exploiting preprocessing strategy to reduce computation time for large problem. In contrast to `boot.rq.pxy` which uses the classical multinomial sampling scheme and is coded in R, this uses the exponentially weighted bootstrap scheme and is coded in fortran and consequently is considerably faster in larger problems.

### Usage

    boot.rq.pwxy(x, y, tau, coef, R = 200, m0 = NULL, eps = 1e-06, ...)

### Arguments

`x` | Design matrix
`y` | response vector
`tau` | quantile of interest
`coef` | point estimate of fitted object
`R` | the number of bootstrap replications desired.
`m0` | constant to determine initial sample size, defaults to sqrt(n*p) but could use some further tuning...
`eps` | tolerance for convergence of fitting algorithm
`...` | other parameters not yet envisaged.

### Details

The fortran implementation is quite similar to the R code for `boot.rq.pxy` except that there is no multinomial sampling. Instead `rexp(n)` weights are used.

### Value

returns a list with elements:

1. coefficientsa matrix of dimension ncol(x) by R

2. nit a 5 by m matrix of iteration counts

3. info an m-vector of convergence flags

### Author(s)

Blaise Melly and Roger Koenker

### References

Chernozhukov, V. I. Fernandez-Val and B. Melly, Fast Algorithms for the Quantile Regression Process, 2019, arXiv, 1909.05782,

Portnoy, S. and R. Koenker, The Gaussian Hare and the Laplacian Tortoise, Statistical Science, (1997) 279-300

### See Also

`boot.rq.pxy`

[Package quantreg version 5.93 Index]