References
- Anselin, L., and R. J. G. M. Florax. 1995. New directions in spatial econometrics. Berlin: Springer.
- Bogachev, V. I. 1999. Gaussian measures. Math surveys and monographs, 62. American Mathematical Society.
- Barrientos, J., F. Ferraty, and P. Vieu. 2010. Locally modelled regression and functional data. Journal of Nonparametric Statistics 22 (5):617–32. doi:10.1080/10485250903089930.
- Baíllo, A., and A. Grané. 2009. Local linear regression for functional predictor and scalar response. Journal of Multivariate Analysis 100 (1):102–11. doi:10.1016/j.jmva.2008.03.008.
- Berlinet, A., A. Elamine, and A. Mas. 2011. Local linear regression for functional data. Annals of the Institute of Statistical Mathematics 63 (5):1047–75. doi:10.1007/s10463-010-0275-8.
- Bosq, D. 2000. Linear processes in function spaces: Theory and applications. Lecture notes in statistics, vol. 149. New York: Springer.
- Biau, G., and B. Cadre. 2004. Nonparametric spatial prediction. Statistical Inference for Stochastic Processes 7 (3):327–49. doi:10.1023/B:SISP.0000049116.23705.88.
- Carbon, M., M. Hallin, and L. T. Tran. 1996. Kernel density estimation for random fields: the L1 -theory. Journal of Nonparametric Statistics 6 (2–3):157–70. doi:10.1080/10485259608832669.
- Carbon, M., L. T. Tran, and B. Wu. 1997. Kernel density estimation for random fields. Statistics & Probability Letters 36 (2):115–25. doi:10.1016/S0167-7152(97)00054-0.
- Chouaf, A., and A. Laksaci. 2012. On the functional local linear estimate for spatial regression. Statistics & Risk Modeling 29 (3):189–214. doi:10.1524/strm.2012.1114.
- Crambes, C., A. Gannoun, and Y. Henchiri. 2013. Support vector machine quantile regression approach for functional data: simulation and application studies. Journal of Multivariate Analysis 121:50–68. doi:10.1016/j.jmva.2013.06.004.
- Cressie, N. A. 1993. Statistics for spatial data. New York: Wiley.
- Dabo-Niang, S., and B. Thiam. 2010. Robust quantile estimation and prediction for spatial processes. Statistics & Probability Letters 80 (17–18):1447–58. doi:10.1016/j.spl.2010.05.012.
- Dabo-Niang, S., M. Rachdi, and A.-F. Yao. 2011. Kernel regression estimation for spatial functional random variables. Far East Journal of Theoretical Statistics 37:77–113.
- Dabo-Niang, S., Z. Kaid, and A. Laksaci. 2012. Spatial conditional quantile regression: weak consistency of a kernel estimate. Revue Roumaine de Mathématique Pures et Appliquées 57:311–39.
- Demongeot, J., A. Laksaci, F. Madani, and M. Rachdi. 2013. Functional data: Local linear estimation of the conditional density and its application. Statistics 47 (1):26–44. doi:10.1080/02331888.2011.568117.
- Doukhan, P. 1994. Mixing: Properties and examples. Lecture notes in statistics, 85. New York: Springer-Verlag.
- Fan, J., and I. Gijbels. 1996. Local polynomial modelling and its applications. London: Chapman & Hall.
- Ferraty, F., and P. Vieu. 2006. Nonparametric functional data analysis. Theory and practice. New York: Springer Series in Statistics.
- Gannoun, A., J. Saracco, and K. Yu. 2003. Nonparametric prediction by conditional median and quantiles. Journal of Statistical Planning and Inference 117 (2):207–23. doi:10.1016/S0378-3758(02)00384-1.
- Geenens, G. 2011. Curse of dimensionality and related issues in nonparametric functional regression. Statistics Surveys 5:30–43. doi:10.1214/09-SS049.
- Hallin, M., Z. Lu, and K. Yu. 2009. Local linear spatial quantile regression. Bernoulli 15 (3):659–86. doi:10.3150/08-BEJ168.
- Helal, N., and E. Ould-Said. 2016. Kernel conditional quantile estimator under left truncation for functional regressors. Opuscula Mathematica 36 (1):25–48. doi:10.7494/OpMath.2016.36.1.25.
- Hsing, T., and R. Eubank. 2015. Theoretical foundations of functional data analysis, with an introduction to linear operators. Wiley series in probability and statistics. Chichester: John Wiley & Sons.
- Horváth, L., and P. Kokoszka. 2012. Inference for functional data with applications. New York: Springer Series in Statistics, Springer.
- Ibragimov, I. A., and Y. V. Linnik. 1971. Independent and stationary sequences of random variables. Groningen: Wolters-Noordhoff.
- Kato, K. 2012. Estimation in functional linear quantile regression. The Annals of Statistics 40 (6):3108–36. doi:10.1214/12-AOS1066.
- Koenker, R., and Q. Zhao. 1996. Conditional quantile estimation and inference for ARCH models. Econometric Theory 12 (5):793–813. doi:10.1017/S0266466600007167.
- Laksaci, A., M. Lemdani, and E. Ould Saïd. 2009. A generalized L1-approach for a kernel estimator of conditional quantile with functional regressors: Consistency and asymptotic normality. Statistics & Probability Letters 79 (8):1065–73. doi:10.1016/j.spl.2008.12.016.
- Liebscher, E. 2001. Estimation of the density and the regression function under mixing conditions. Statistics and Decisions 19:9–26.
- Rachdi, M., A. Laksaci, J. Demongeot, A. Abdali, and F. Madani. 2014. Theoretical and practical aspects of the quadratic error in the local linear estimation of the conditional density for functional data. Computational Statistics – Data Analysis 73: 53–68.
- Rahmani, S., A. Laksaci, and M. Rachdi. 2013. Spatial modelization: Local linear estimation of the conditional distribution for functional data. Spatial Statistics 6:1–23. doi:10.1016/j.spasta.2013.04.004.
- Ramsay, J. O., and B. W. Silverman. 2002. Applied functional data analysis. Methods and case studies. New York: Springer Series in Statistics..
- Ripley, B. 1981. Spatial statistics. New-York: Wiley.
- Stone, C. J. 1977. Consistent nonparametric regression. The Annals of Statistics 5 (4):595–645. doi:10.1214/aos/1176343886.
- Stute, W. 1986. Conditional empirical processes. The Annals of Statistics 14 (2):638–47. doi:10.1214/aos/1176349943.
- Tran, L. T. 1990. Kernel density estimation on random fields. Journal of Multivariate Analysis 34 (1):37–53. doi:10.1016/0047-259X(90)90059-Q.
- Wang, K., and L. Lin. 2015. Variable selection in semiparametric quantile modeling for longitudinal data. Communications in Statistics - Theory and Methods 44 (11):2243–66. doi:10.1080/03610926.2013.857418.
- Yu, K., Z. Lu, and J. Stander. 2003. Quantile regression: applications and current research areas. Journal of the Royal Statistical Society: Series D (the Statistician) 52 (3):331–50. doi:10.1111/1467-9884.00363.
- Zhang, J. 2014. Analysis of variance for functional data. Monographs on statistics and applied probability, vol. 127. Boca Raton, FL: CRC Press.