[
[1] A. Aït Saidi, F. Ferraty and R. Kassa, Single functional index model for a time series, Revue Roumaine de Mathématique Pures Appliquées, 50 (2002), 321–330.
]Search in Google Scholar
[
[2] A. Aït Saidi, F. Ferraty, R. Kassa and P. Vieu, Cross-validated estimation in the single functional index model, Statistics, 42(6) (2008), 475–494.10.1080/02331880801980377
]Search in Google Scholar
[
[3] S. Attaouti, A. Laksaci and E. Ould-Saïd, A note on the conditional density estimate in the single functional index model, Statistics and Probability Letters, 81(1) (2011), 45–53.10.1016/j.spl.2010.09.017
]Search in Google Scholar
[
[4] S. Attaoui, On the Nonparametric Conditional Density and Mode Estimates in the Single Functional Index Model with Strongly Mixing Data, Sankhyã: The Indian Journal of Statistics, 76(A) (2014), 356–378.10.1007/s13171-014-0051-6
]Search in Google Scholar
[
[5] S. Attaoui and N. Ling, Asymptotic Results of a Nonparametric Conditional Cumulative Distribution Estimator in the single functional index Modeling for Time Series Data with Applications, Metrika, 79 (2016), 485–511.10.1007/s00184-015-0564-6
]Search in Google Scholar
[
[6] Z. Cai, Estimating a distribution function for censored time series data, J. Multivariate Anal., 78 (2001), 299–318.10.1006/jmva.2000.1953
]Search in Google Scholar
[
[7] Z. Cai, Regression quantiles for time series, Econometric Theory, 18 (2002), 169–192.10.1017/S0266466602181096
]Search in Google Scholar
[
[8] M. Chaouch and S. Khardani, Randomly Censored Quantile Regression Estimation using Functional Stationary Ergodic Data, Journal of Non-parametric Statistics, 27 (2015), 65–87.10.1080/10485252.2014.982651
]Search in Google Scholar
[
[9] P. Chaudhuri, K. Doksum and A. Samarov, On average derivative quantile regression, Ann. Statist., 25 (1997),715–744.10.1214/aos/1031833670
]Search in Google Scholar
[
[10] J. Dedecker, P. Doukhan, G. Lang, J.R. Leon, S. Louhichi and C. Prieur, Weak Dependence: With Examples and Applications, Lecture Notes in Statistics, 190. New York: Springer-Verlag, 2007.
]Search in Google Scholar
[
[11] P. Doukhan, Mixing: Properties and Examples, Lecture Notes in Statistics, 85. New York: Springer-Verlag, 1994.
]Search in Google Scholar
[
[12] J. L. Doob, Stochastic Processes, New York: Wiley, 1953.
]Search in Google Scholar
[
[13] A. El Ghouch and I. Van Keilegom, Local Linear Quantile Regression with Dependent Censored Data, Statistica Sinica, 19 (2009), 1621–1640.
]Search in Google Scholar
[
[14] M. Ezzahrioui and E. Ould-Saïd, Asymptotic results of a nonparametric conditional quantile estimator for functional time series, Comm. Statist. Theory and Methods., 37(16-17) (2008), 2735–2759.10.1080/03610920802001870
]Search in Google Scholar
[
[15] F. Ferraty, A. Rabhi and P. Vieu, Conditional quantiles for functional dependent data with application to the climatic ElNinô phenomenon, Sankhyã B, Special Issue on Quantile Regression and Related Methods, 67(2) (2005), 378–399.
]Search in Google Scholar
[
[16] F. Ferraty and P. Vieu, Nonparametric Functional Data Analysis: Theory and Practice, Springer Series in Statistics, Springer, New York, 2006.
]Search in Google Scholar
[
[17] A. Gannoun, J. Saracco and K. Yu, Nonparametric prediction by conditional median and quantiles, J. Statist. Plann. Inference., 117 (2003), 207–223.10.1016/S0378-3758(02)00384-1
]Search in Google Scholar
[
[18] N. Kadiri, A. Rabhi and A. Bouchentouf, Strong uniform consistency rates of conditional quantile estimation in the single functional index model under random censorship, Journal Dependence Modeling, 6(1) (2018), 197–227.10.1515/demo-2018-0013
]Search in Google Scholar
[
[19] E. Kaplan and P. Meier, Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association, 53 (1958), 457–481.10.1080/01621459.1958.10501452
]Search in Google Scholar
[
[20] J-P. Lecoutre and E. Ould-Saïd, Hazard rate estimation for strong mixing and censored processes, J. Nonparametr. Stat. 5 (1995), 83–89.10.1080/10485259508832636
]Search in Google Scholar
[
[21] H.Y. Liang and J. Ua-lvarez, Asymptotic Properties of Conditional Quantile Estimator for Censored Dependent Observations, Annals of the Institute of Statistical Mathematics, 63 (2011), 267–289.10.1007/s10463-009-0230-8
]Search in Google Scholar
[
[22] Z. Lin and C. Lu, Limit theory of mixing dependent random variables, Mathematics and its applications, Sciences Press, Kluwer Academic Publishers, Beijing, 1996.
]Search in Google Scholar
[
[23] N. Ling and Q. Xu, Asymptotic normality of conditional density estimation in the single index model for functional time series data, Statistics and Probability Letters, 82 (2012), 2235–2243.10.1016/j.spl.2012.08.018
]Search in Google Scholar
[
[24] N. Ling, Z. Li and W. Yang, Conditional density estimation in the single functional index model for α -mixing functional data, Communications in Statistics: Theory and Methods, 43(3) (2014), 441–454.10.1080/03610926.2012.664236
]Search in Google Scholar
[
[25] E. Masry and D. Tøjstheim, Nonparametric estimation and identification of nonlinear time series, Econometric Theor., 11 (1995), 258–289.10.1017/S0266466600009166
]Search in Google Scholar
[
[26] E. Masry, Nonparametric Regression Estimation for Dependent Functional Data: Asymptotic Normality, Stochastic Processes and their Applications, 115 (2005), 155–177.10.1016/j.spa.2004.07.006
]Search in Google Scholar
[
[27] C. Muharisa, F. Yanuar and D. Devianto, Simulation Study The Using of Bayesian Quantile Regression in Non-normal Error, Cauchy: Jurnal Matematika Murni dan Aplikasi, 5(3) (2018), 121–126.10.18860/ca.v5i3.5633
]Search in Google Scholar
[
[28] E. Ould-Saïd, A Strong Uniform Convergence Rate of Kernel Conditional Quantile Estimator under Random Censorship, Statistics and Probability Letters, 76 (2006), 579–586.10.1016/j.spl.2005.09.002
]Search in Google Scholar
[
[29] W-J. Padgett, Nonparametric Estimation Of Density And Hazard Rate Functions When Samples Are Censored, In P.R. Krishnaiah and C.R. Rao (Eds). Handbook of Statist. 7, pp. 313–331. Elsevier/North-Holland. Amsterdam. Science Publishers, 1988.10.1016/S0169-7161(88)07018-X
]Search in Google Scholar
[
[30] G. Roussas, Nonparametric estimation of the transition distribution function of a Markov process, Ann. Statist., 40 (1969), 1386–1400.10.1214/aoms/1177697510
]Search in Google Scholar
[
[31] M. Samanta, Nonparametric estimation of conditional quantiles, Statist. Probab. Lett., 7 (1989), 407–412.10.1016/0167-7152(89)90095-3
]Search in Google Scholar
[
[32] S. Sarmada and F. Yanuar, Quantile Regression Approach to Model Censored Data, Science and Technology Indonesia, 5(3) (2020), 79–84.10.26554/sti.2020.5.3.79-84
]Search in Google Scholar
[
[33] M. Tanner and W-H. Wong, The estimation of the hazard function from randomly censored data by the kernel methods, Ann. Statist., 11 (1983), 989–993.
]Search in Google Scholar
[
[34] I. Van-Keilegom and N. Veraverbeke, Hazard rate estimation in nonpara-metric regression with censored data, Ann. Inst. Statist. Math., 53 (2001), 730–745.10.1023/A:1014696717644
]Search in Google Scholar
[
[35] H. Wang and Y. Zhao, A kernel estimator for conditional t-quantiles for mixing samples and its strong uniform convergence, (in chinese), Math. Appl. (Wuhan)., 12 (1999), 123–127.
]Search in Google Scholar
[
[36] F. Yanuar, H. Laila and D. Devianto, The Simulation Study to Test the Performance of Quantile Regression Method With Heteroscedastic Error Variance, Cauchy: Jurnal Matematika Murni dan Aplikasi, 5(1) (2017), 36–41.10.18860/ca.v5i1.4209
]Search in Google Scholar
[
[37] F. Yanuar, H. Yozza, F. Firdawati, I. Rahmi, and A. Zetra, Applying Bootstrap Quantile Regression for The Construction of a Low Birth Weight Model, Makara Journal of Health Research, 23(2) (2019), 90–95.10.7454/msk.v23i2.9886
]Search in Google Scholar
[
[38] Y. Zhou and H. Liang, Asymptotic properties for L1 norm kernel estimator of conditional median under dependence, J. Nonparametr. Stat., 15 (2003), 205–219.10.1080/1048525031000089293
]Search in Google Scholar