quantreg: rq.fit.ppro – R documentation

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rq.fit.ppro

Preprocessing fitting method for QR

Description

Preprocessing method for fitting quantile regression models that exploits the fact that adjacent tau's should have nearly the same sign vectors for residuals.

Usage

rq.fit.ppro(x, y, tau, weights = NULL, Mm.factor = 0.8, eps = 1e-06, ...)

Arguments

`x`	Design matrix
`y`	Response vector
`tau`	quantile vector of interest
`weights`	case weights
`Mm.factor`	constant determining initial sample size
`eps`	Convergence tolerance
`...`	Other arguments

Details

See references for further details.

Value

Returns a list with components:

`coefficients`	Matrix of coefficient estimates
`residuals`	Matrix of residual estimates
`rho`	vector of objective function values
`weights`	vector of case weights

Author(s)

Blaise Melly and Roger Koenker

References

Chernozhukov, V. I. Fernandez-Val and B. Melly, Fast Algorithms for the Quantile Regression Process, 2020, Empirical Economics.,

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

Roger Koenker [cre, aut], Stephen Portnoy [ctb] (Contributions to Censored QR code), Pin Tian Ng [ctb] (Contributions to Sparse QR code), Blaise Melly [ctb] (Contributions to preprocessing code), Achim Zeileis [ctb] (Contributions to dynrq code essentially identical to his dynlm code), Philip Grosjean [ctb] (Contributions to nlrq code), Cleve Moler [ctb] (author of several linpack routines), Yousef Saad [ctb] (author of sparskit2), Victor Chernozhukov [ctb] (contributions to extreme value inference code), Ivan Fernandez-Val [ctb] (contributions to extreme value inference code), Brian D Ripley [trl, ctb] (Initial (2001) R port from S (to my everlasting shame -- how could I have been so slow to adopt R!) and for numerous other suggestions and useful advice)

Initial release