Local linear estimation of the conditional quantile for censored data and functional regressors

Leulmi, Sara

doi:10.1080/03610926.2019.1692033

Cited by 7 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remark 1: Hypotheses H1-H6 have already been used in literature, we refer them to Messaci et al (2015) and Leulmi (2019).…”

Section: H3mentioning

confidence: 99%

“…All these studies were carried out in the case of complete data. Concerning the incomplete data and when the regressors are of functional type, we can refer to Leulmi (2019) and Leulmi (2020). She obtained the rate of the pointwise and the uniform almost complete convergence of a local linear estimator of the conditional quantile and the regression function and she improved that the local linear method outperforms the kernel method even for censored data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A non-parametric estimation of the conditional quantile for truncated and functional data

Boudada¹

2022

IJMOR

View full text Add to dashboard Cite

In this paper, we propose a local linear estimator for the conditional distribution function in the case where the real response variable is subject to left-truncation by another random variable (r.v.) and the covariate is of functional type. Under regular assumptions, both of the pointwise and the uniform almost sure convergences, of the proposed estimator, are established. Then, we deduce the uniform almost sure convergence of the obtained conditional quantile estimator. A simulation study is used to illustrate the performance of our estimator with respect to the kernel method.

show abstract

“…Remark 1: Hypotheses H1-H6 have already been used in literature, we refer them to Messaci et al (2015) and Leulmi (2019).…”

Section: H3mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A non-parametric estimation of the conditional quantile for truncated and functional data

Boudada¹

2022

IJMOR

View full text Add to dashboard Cite

show abstract

“…Based on the local linear approach, we can refer to [16,17], where the rates of the pointwise and the uniform almost complete convergences of the conditional quantile and the regression estimators are obtained for functional censored independent data. [3] constructed a conditional density's estimator and established its pointwise almost sure convergence, for functional censored data under dependence condition.…”

Section: Introductionmentioning

confidence: 99%

Local linear estimation of the conditional mode under left truncation for functional regressors

Boudada,

Leulmi

2023

Kybernetika

View full text Add to dashboard Cite

In this work, we introduce a local linear estimator of the conditional mode for a random real response variable which is subject to left-truncation by another random variable where the covariate takes values in an infinite dimensional space. We first establish both of pointwise and uniform almost sure convergences, with rates, of the conditional density estimator. Then, we deduce the strong consistency of the obtained conditional mode estimator. We finally illustrate the outperformance of our method with respect to the kernel one through a simulation study for a finite sample with different rates of truncation and sizes.

show abstract

Local linear estimation of the conditional cumulative distribution function: Censored functional data case

Rahmani

Bouanani

2022

Sankhya A

View full text Add to dashboard Cite

Local linear estimation of the conditional quantile for censored data and functional regressors

Cited by 7 publications

References 10 publications

A non-parametric estimation of the conditional quantile for truncated and functional data

A non-parametric estimation of the conditional quantile for truncated and functional data

Local linear estimation of the conditional mode under left truncation for functional regressors

Local linear estimation of the conditional cumulative distribution function: Censored functional data case

Contact Info

Product

Resources

About