Version 1
: Received: 24 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (13:24:46 CEST)
How to cite:
Elbayoumi, T.; Mostafa, S. Bias Analysis and Correction in Weighted-L1 Estimators for the First-Order Bifurcating Autoregressive Model. Preprints2024, 2024092024. https://doi.org/10.20944/preprints202409.2024.v1
Elbayoumi, T.; Mostafa, S. Bias Analysis and Correction in Weighted-L1 Estimators for the First-Order Bifurcating Autoregressive Model. Preprints 2024, 2024092024. https://doi.org/10.20944/preprints202409.2024.v1
Elbayoumi, T.; Mostafa, S. Bias Analysis and Correction in Weighted-L1 Estimators for the First-Order Bifurcating Autoregressive Model. Preprints2024, 2024092024. https://doi.org/10.20944/preprints202409.2024.v1
APA Style
Elbayoumi, T., & Mostafa, S. (2024). Bias Analysis and Correction in Weighted-L1 Estimators for the First-Order Bifurcating Autoregressive Model. Preprints. https://doi.org/10.20944/preprints202409.2024.v1
Chicago/Turabian Style
Elbayoumi, T. and Sayed Mostafa. 2024 "Bias Analysis and Correction in Weighted-L1 Estimators for the First-Order Bifurcating Autoregressive Model" Preprints. https://doi.org/10.20944/preprints202409.2024.v1
Abstract
This study examines the bias in weighted least absolute deviation (WL1) estimation within the context of stationary first-order bifurcating autoregressive (BAR(1)) models, which are frequently employed to analyze binary tree-like data, including applications in cell lineage studies. Initial findings indicate that WL1 estimators can demonstrate substantial and problematic biases, especially when small to moderate sample sizes. The autoregressive parameter and the correlation between model errors influence the volume and direction of the bias. To address this issue, we propose two bootstrap-based bias-corrected estimators for the WL1 estimator. We conduct extensive simulations to assess the performance of these bias-corrected estimators. Our empirical findings demonstrate that these estimators effectively reduce the bias inherent in WL1 estimators, with their performance being particularly pronounced at the extremes of the autoregressive parameter range.
Keywords
Bifurcating; Autoregressive; Singe Bootstrap; Fast Double Bootstrap
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
