Work

Random Weighting Approximation of M-estimators with Increasing Dimensions of Parameter

Year: 2025

Type: article

Abstract: The random weighting (RW) approach, recognized as a flexible alternative to the classical bootstrap method, has been widely employed for approximating the distribution of parameter estimates.Nevertheless, existing theoretical results for the RW approach primarily address scenarios where the parameter dimension remains fixed.In this paper, we investigate the RW method of M-estimators under general parametric models with increasing dimensions.We establish that the RW estimator has the same asymptotic distribution as that of the parameter M-estimate, which suggests that statistical inference regarding the parameter M-estimate can proceed without estimating nuisance parameters involved in its asymptotic distribution.Statistical properties of the RW estimator, such as the Bahadur representation and convergence rate, are also established.Furthermore, † Co-first authors and contributed equally to this work.

Source: Statistica Sinica

DOI: 10.5705/ss.202025.0132

Authors: Ruixing Ming, Chengyao Yu, Min Xiao, Zhanfeng Wang

Institutions: Zhejiang Gongshang University, University of Science and Technology of China

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funders: Zhejiang Gongshang University, National Natural Science Foundation of China

Awards: G682679130, G8708097720