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Main Authors: Bamio, David, de Uña-Álvarez, Jacobo
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2601.09576
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author Bamio, David
de Uña-Álvarez, Jacobo
author_facet Bamio, David
de Uña-Álvarez, Jacobo
contents In Astronomy, Survival Analysis and Epidemiology, among many other fields, doubly truncated data often appear. Double truncation generally induces a sampling bias, so ordinary estimators may be inconsistent. In this paper, smoothing spline density estimation from doubly truncated data is investigated. For this purpose, an appropriate correction of the penalized likelihood that accounts for the sampling bias is considered. The theoretical properties of the estimator are discussed, and its practical performance is evaluated through simulations. Two real datasets are analyzed using the proposed method for illustrative purposes. Comparison to kernel density smoothing is included.
format Preprint
id arxiv_https___arxiv_org_abs_2601_09576
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Smoothing spline density estimation from doubly truncated data
Bamio, David
de Uña-Álvarez, Jacobo
Methodology
62G07
In Astronomy, Survival Analysis and Epidemiology, among many other fields, doubly truncated data often appear. Double truncation generally induces a sampling bias, so ordinary estimators may be inconsistent. In this paper, smoothing spline density estimation from doubly truncated data is investigated. For this purpose, an appropriate correction of the penalized likelihood that accounts for the sampling bias is considered. The theoretical properties of the estimator are discussed, and its practical performance is evaluated through simulations. Two real datasets are analyzed using the proposed method for illustrative purposes. Comparison to kernel density smoothing is included.
title Smoothing spline density estimation from doubly truncated data
topic Methodology
62G07
url https://arxiv.org/abs/2601.09576