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Non-crossing polynomial quantile regression

versión 1.1.0.0 (2.82 KB) por Wolfgang Schwanghart
Non-crossing polynomial quantile regression

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Actualizada 17 Jan 2016

De GitHub

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ncquantreg finds the coefficients of a polynomial p(x) of degree n that fits the data in vector x to the quantiles tau of y.
ncquantreg(x,y) performs median regression (tau = 0.5) using a polynomial of degree n=1.
ncquantreg(x,y,n,tau) fits numel(tau) polynomials with degree n. The algorithm uses a stepwise multiple quantile regression estimation using non-crossing constraints (Wu and Liu, 2009). The approach is stepwise in a sense that a quantile function is estimated so that it does not cross with a function fitted in a previous step. The algorithm starts from the middle quantile (i.e. the one closest to 0.5) and than progressivly works through the quantiles with increasing distance from the middle.

ncquantreg(x,y,n,tau,pn,pv,...) takes several parameter name value pairs that control the algorithm and plotting.
Reference

Wu, Y., Liu, Y., 2009. Stepwise multiple quantile regression estimation using non-crossing constraints. Statistics and its Interface 2, 299–310.

Citar como

Wolfgang Schwanghart (2022). Non-crossing polynomial quantile regression (https://github.com/wschwanghart/ncquantreg), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2012a
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux
Agradecimientos

Inspirado por: quantreg(x,y,tau,order,Nboot)

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