D. J. Aigner, T. Amemiya, and D. J. Poirier, On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function, International Economic Review, vol.17, issue.2, p.377, 1976.
DOI : 10.2307/2525708

M. Buchinsky, Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research, The Journal of Human Resources, vol.33, issue.1, p.88126, 1998.
DOI : 10.2307/146316

A. Ivan and . Canay, A simple approach to quantile regression for panel data

D. Card, Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems, NBER Working Papers National Bureau of Economic Research, vol.7769, 2000.

B. Efron, Regression percentiles using asymmetric squared error loss, Statistica Sinica, vol.1, issue.1, p.93125, 1991.

B. Fitzenberger, The moving blocks bootstrap and robust inference for linear least squares and quantile regressions, Journal of Econometrics, vol.82, issue.2, p.235287, 1998.
DOI : 10.1016/S0304-4076(97)00058-4

N. A. Furlotte, E. Eskin, and S. Eyheramendy, Genomewide association mapping with longitudinal data, Genetic Epidemiology, vol.36, issue.5, p.463471, 2012.
DOI : 10.1002/gepi.21640
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3625633

A. Gelman and J. Hill, Data analysis using regression and multilevel/hierarchical models, volume Analytical methods for social research, 2007.
DOI : 10.1017/cbo9780511790942
URL : https://repozitorij.uni-lj.si/Dokument.php?id=69512

M. Geraci and M. Bottai, Quantile regression for longitudinal data using the asymmetric Laplace distribution, Biostatistics, vol.8, issue.1, p.14015445, 1996.
DOI : 10.1093/biostatistics/kxj039

M. Geraci and M. Bottai, Linear quantile mixed models, Statistics and Computing, vol.72, issue.3, p.461479, 2014.
DOI : 10.1007/s11222-013-9381-9
URL : http://discovery.ucl.ac.uk/1324520/2/lqmm_preprint_110601.pdf

M. Harding and C. Lamarche, A quantile regression approach for estimating panel data models using instrumental variables, Economics Letters, vol.104, issue.3, p.133135, 1996.
DOI : 10.1016/j.econlet.2009.04.025

C. Hsiao, Panel data analysisadvantages and challenges, TEST, vol.16, issue.1, p.122, 2007.

T. Kneib, Beyond mean regression, Statistical Modelling, vol.13, issue.4, p.275303, 2013.
DOI : 10.1177/1471082X13494159
URL : http://resolver.sub.uni-goettingen.de/purl?gs-1/10831

R. Koenker and Y. Bilias, Quantile regression for duration data: A reappraisal of the pennsylvania reemployment bonus experiments, Empirical Economics, vol.26, issue.1, 2001.

R. Koenker, When are expectiles percentiles? Econometric Theory, p.526527, 1993.
DOI : 10.1017/s0266466600013049

R. Koenker, Quantile regression for longitudinal data, Journal of Multivariate Analysis, vol.91, issue.1, pp.2004-2014
DOI : 10.1016/j.jmva.2004.05.006

R. Koenker, Quantile regression, 2005.

R. Koenker, quantreg: Quantile Regression, 2016. R package version 5

R. Koenker and S. H. Bache, rqpd: Regression Quantiles for Panel Data, 2014.

R. Koenker, G. Bassett, and J. , Regression quantiles, Econometrica . Journal of the Econometric Society, vol.46, issue.1, p.3350, 1978.
DOI : 10.2307/1913643

G. Koop and J. L. Tobias, Learning about heterogeneity in returns to schooling, Journal of Applied Econometrics, vol.43, issue.7, p.827849, 2004.
DOI : 10.1002/jae.744

C. Lamarche, Robust penalized quantile regression estimation for panel data, Journal of Econometrics, vol.157, issue.2, pp.2010-2018
DOI : 10.1016/j.jeconom.2010.03.042

A. F. José, J. M. Machado, and S. Silva, Quantiles for counts, Journal of the American Statistical Association, vol.100, issue.472, p.12261237, 2005.

Y. Mu, M. Kocherginsky, and X. He, Practical condence intervals for regression quantiles, Journal of Computational and Graphical Statistics, vol.14, issue.1, p.4155, 2005.

K. Whitney, J. L. Newey, and . Powell, Asymmetric least squares estimation and testing, Econometrica, vol.55, issue.4, p.81947, 1987.

J. L. Powell, Censored regression quantiles, Journal of Econometrics, vol.32, issue.1, p.143155, 1986.
DOI : 10.1016/0304-4076(86)90016-3

R. Team, R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing, 2016.

K. J. Rothman and T. L. , Lash Associate Professor, and Sander Greenland. Modern Epidemiology, pp.2012-2024

S. Schnabel and P. Eilers, Optimal expectile smoothing, Computational Statistics & Data Analysis, vol.53, issue.12, p.41684177, 2009.
DOI : 10.1016/j.csda.2009.05.002

F. Sobotka, G. Kauermann, L. S. Waltrup, and T. Kneib, On condence intervals for semiparametric expectile regression, Statistics and Computing, vol.23, issue.2, p.135148, 2013.

F. Sobotka and T. Kneib, Geoadditive expectile regres- sion
DOI : 10.1016/j.csda.2010.11.015

L. Schulze-waltrup, F. Sobotka, T. Kneib, and G. Kauermann, Expectile and quantile regressiondavid and goliath? Statistical Modelling, pp.1471082-14561155

K. Yu, Z. Lu, and J. Stander, Quantile regression: applications and current research areas, Journal of the Royal Statistical Society: Series D (The Statistician), vol.93, issue.3, p.331350, 2003.
DOI : 10.1016/S0167-7152(01)00124-9