quantreg.m - quantile regression 1.0

Quantile Regression USAGE: [p,stats]=quantreg(x,y,tau[,order,nboot]); INPUTS: x,y: data that is fitted. (x and y should be columns) Note: that if x is a matrix with several columns then multiple linear regression is used and the "order" argument is not used. tau: quantile used in regression. order: polynomial order. (default=1) nboot: number of bootstrap surrogates used in statistical inference.(default=200) stats is a structure with the following fields: .pse: standard error on p. (not independent) .pboot: the bootstrapped polynomial coefficients. .yfitci: 95% confidence interval on polyval(p,x) Note: uses bootstrap on residuals for statistical inference. (see help bootstrp) check also: http://www.econ.uiuc.edu/~roger/research/intro/rq.pdf EXAMPLE: x=(1:1000)'; y=randn(size(x)).*(1+x/300)+(x/300).^2; [p,stats]=quantreg(x,y,.9,2); plot(x,y,x,polyval(p,x),x,stats.yfitci,'k:') legend('data','2nd order 90th percentile fit','95% confidence interval','location','best') For references on the method check e.g. and refs therein: http://www.econ.uiuc.edu/~roger/research/rq/QRJEP.pdf Copyright (C) 2008, Aslak Grinsted

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Bootstrp,  Confidence,  Error,  Independent,  Inference,  Interval,  Pboot,  Polyvalpx,  Residuals,  Yfitci
Users rating:	0/10