RT Journal Article
T1 Quantile regression: estimation and lack-of-fit tests
A1 Conde Amboage, Mercedes
A1 González Manteiga, Wenceslao
A1 Sánchez Sellero, César
K1 Quantile regression
K1 Estimation
K1 Lack-of-fit tests
K1 Robustness
K1 Sparsity
AB Although mean regression achieved its greatest diffusion in the twentieth century, it is very surprising to observe that the ideas of quantileregression appeared earlier. While the beginning of the least-squares regression can be dated in the year 1805 by the work of Legendre, in themid-eighteenth century Boscovich already adjusted data on the ellipticityof the Earth using concepts of quantile regression.Quantile regression is employed when the aim of the study is centred on the estimation of the different positions (quantiles). This kind ofregression allows a more detailed description of the behaviour of the response variable, adapts to situations under more general conditions of theerror distribution and enjoys robustness properties. For all that, quantileregression is a very useful statistical technology for a large diversity ofdisciplines. In this paper a review on quantile regression methods will bepresented
PB Sociedad de Estadística e Investigación Operativa
SN 2387-1725
YR 2018
FD 2018
LK http://hdl.handle.net/10347/18607
UL http://hdl.handle.net/10347/18607
LA eng
NO Conde-Amboage, M., González-Manteiga, W. & Sánchez-Sellero, C. (2018). Quantile regression: estimation and lack-of-fit tests. Boletín de Estadística e Investigación Operativa. Vol. 34, no. 2, pp. 97-116
DS Minerva
RD 28 abr 2026