删失指标随机缺失下一般线性模型的加权最小二乘估计

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

Zhenmin Rao, Jiangfeng Wang, Kang Hu, Shan He. 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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References 10

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

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