 Nonparametric estimation of the relative error regression for twice censored and dependent dataSabrina Benzamouche, Elias Ould Saïd and Ourida Sadki Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 5, 1492-1525 Abstract: Let (Yi)1≤i≤n be a sequence of dependent random variables (r.v.) of interest distributed as Y and (Xi)1≤i≤n be a corresponding d−dimensional vector of covariates taking values on ℝd. The r.v. Y is twice censored and satisfies the α−mixing property. In this article, we are interested in the relative error regression estimation in the case of twice-censored data under strong mixing conditions. Under suitable assumptions, the estimator’s strong uniform almost sure consistency with rate and asymptotic normality are established. We have highlighted the covariance term, which is relatively uncommon in this context. Furthermore, we give a result about the α-mixing rate in the case of twice-censoring. As far as we know, there is no analogous result with proof in the context of twice-censoring. A simulation study is conducted in both one and two dimensions for the covariate to highlight the performance of our estimator. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:54:y:2025:i:5:p:1492-1525 Ordering information: This journal article can be ordered from DOI: 10.1080/03610926.2024.2344575 Access Statistics for this article Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals |
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