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Acta mathematica scientia,Series A ›› 2025, Vol. 45 ›› Issue (3): 919-933.

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Least Squares Estimators of General Linear Model with Censoring Indicators Missing at Random

Rao Zhenmin,Wang Jiangfeng^*(),Hu Kang,He Shan

School of Statistic and Mathematics,Zhejiang Gongshang University, Hangzhou 310018

Received:2024-07-16 Revised:2024-11-07 Online:2025-06-26 Published:2025-06-20
Supported by:
National Key R&D Program of China(2024YFA1013502);NSFC(U23A2064);NSFC(12031005);Zhejiang Province Natural Science Foundation(LY24A010004);Teacher Professional Development Program for Domestic Visiting Scholars(FX2023017)

Abstract

Abstract:

This article investigates the weighted least squares regression estimators of general linear models with censoring indicators missing at random. Based on three weighting methods of calibration, interpolation, and inverse probability, parameter estimators are constructed respectively. Under appropriate assumptions, asymptotic normality of these estimators has been established, and a new bootstrap testing program based on least squares weighted residual (LSWR) is proposed. Finally, the effectiveness of these estimators and testing procedures are analyzed through numerical simulations and actual data.

Key words: censoring indicator, missing at random, general linear models, asymptotic normality, bootstrap testing

CLC Number:

O212.73

Rao Zhenmin, Wang Jiangfeng, Hu Kang, He Shan. Least Squares Estimators of General Linear Model with Censoring Indicators Missing at Random[J].Acta mathematica scientia,Series A, 2025, 45(3): 919-933.

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Least Squares Estimators of General Linear Model with Censoring Indicators Missing at Random

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