Nonparametric robust regression estimation for censored data

Lemdani, Mohamed; Saïd, Elias Ould

doi:10.1007/s00362-015-0709-8

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Carbonez el al. (1995), Kohler et al (2002) Guessoum and Ould Saïd (2012) and Lemdani and Ould Saïd (2013) among others) use the so-called synthetic data which allow to take into account the censoring effect on the lifetime distribution. To make clear this notion, let…”

Section: Robust Regression Estimation With Censored Datamentioning

confidence: 99%

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Nonparametric M-estimation for right censored regression model with stationary ergodic data

Chaouch¹,

Laïb²,

Ould-Saı̈d³

2016

Preprint

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The present paper deals with a nonparametric M -estimation for right censored regression model with stationary ergodic data. Defined as an implicit function, a kernel type estimator of a family of robust regression is considered when the covariate take its values in R d (d ≥ 1) and the data are sampled from stationary ergodic process. The strong consistency (with rate) and the asymptotic distribution of the estimator are established under mild assumptions. Moreover, a usable confidence interval is provided which does not depend on any unknown quantity. Our results hold without any mixing condition and do not require the existence of marginal densities. A comparison study based on simulated data is also provided.

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Section: Robust Regression Estimation With Censored Datamentioning

confidence: 99%

“…(2010) and Lemdani and Ould Saïd (2013) to study the conditional mode and the robust regression estimators respectively under an α-mixing and iid cases respectively. Here a more general dependence framework (ergodicity) is considered.…”

Section: Confidence Intervalsmentioning

confidence: 99%

Nonparametric M-estimation for right censored regression model with stationary ergodic data

Chaouch¹,

Laïb²,

Ould-Saı̈d³

2016

Preprint

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“…See for instance Li and Zheng (2009), Lemdani and Saïd (2015) and Luo and Zhang (2015). In this context, the aim of this work is the estimation of the local variance function when homoscedasticity is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Partitioning estimation of local variance based on nearest neighbors under censoring

Ferrario

2016

Stat Papers

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In a nonparametric and heteroscedastic setting, our primary interest is in the local variance estimation when the response variable is subject to right censoring. For the proposed partitioning local variance estimators, based on the first and second nearest neighbors, some transformations on the observed censoring times are involved, using their estimated survival functions. Proofs of consistency and rate of convergence for the presented estimators are given. Moreover, local variance estimation is demonstrated on the basis of real survival data.

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The estimation for the general additive–multiplicative hazard model using the length-biased survival data

Zhou

2019

Stat Papers

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Nonparametric robust regression estimation for censored data

Cited by 9 publications

References 31 publications

Nonparametric M-estimation for right censored regression model with stationary ergodic data

Nonparametric M-estimation for right censored regression model with stationary ergodic data

Partitioning estimation of local variance based on nearest neighbors under censoring

The estimation for the general additive–multiplicative hazard model using the length-biased survival data

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