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Original Articles

On the Nonparametric Estimation of the Functional ψ-Regression for a Random Left-Truncation Model

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Pages 823-849 | Received 10 Jul 2014, Accepted 18 Mar 2015, Published online: 28 May 2015

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Asma Gheliem & Zohra Guessoum. (2022) Simulating the behavior of a kernel M-estimator for left-truncated and associated model. Communications in Statistics - Simulation and Computation 0:0, pages 1-23.
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Hassiba Benseradj & Zohra Guessoum. (2022) Strong uniform consistency rate of an M-estimator of regression function for incomplete data under α-mixing condition. Communications in Statistics - Theory and Methods 51:7, pages 2082-2115.
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Mustapha Rachdi, Ali Laksaci, Ibrahim M. Almanjahie & Zouaoui Chikr-Elmezouar. (2020) FDA: theoretical and practical efficiency of the local linear estimation based on the kNN smoothing of the conditional distribution when there are missing data. Journal of Statistical Computation and Simulation 90:8, pages 1479-1495.
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Nengxiang Ling & Philippe Vieu. (2018) Nonparametric modelling for functional data: selected survey and tracks for future. Statistics 52:4, pages 934-949.
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Belkais Altendji, Jacques Demongeot, Ali Laksaci & Mustapha Rachdi. (2018) Functional data analysis: estimation of the relative error in functional regression under random left-truncation model. Journal of Nonparametric Statistics 30:2, pages 472-490.
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Nengxiang Ling & Yang Liu. (2017) The kernel regression estimation for randomly censored functional stationary ergodic data. Communications in Statistics - Theory and Methods 46:17, pages 8557-8574.
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Articles from other publishers (1)

Saliha Derrar, Ali Laksaci & Elias Ould Saïd. (2018) M-estimation of the regression function under random left truncation and functional time series model. Statistical Papers 61:3, pages 1181-1202.
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