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Journal of Statistics and Management Systems

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

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Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

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Journal of Statistics and Management Systems
Open Access Research Article

Inference for normal data under MAR missingness

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pp. 1–31Online FirstMay 2026DOI: 10.47974/JSMS-1649XML
Received:
01 Dec 2025
Published Online:
22 May 2026
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1649
Pages:
1–31

Abstract

 In this paper, considering a regression setup with the outcome following a normal distribution, the presence of missing observations has been assessed by assuming that they are missing at random, i.e., the setup involves MAR conditions (Rubin [1]). Here, a logistic regression scheme is employed to model the missingness probabilities, along with the formulation of the joint probability distribution of the observed data and missingness indicators. Moreover, assuming that the parameters are distinct, it is established that for likelihood-based inference the missingness approach is quite ignorable. By virtue of the EM algorithm, a maximum likelihood estimation process is developed. A simulation study is performed to compare the bias and variance in the settings of MAR and MNAR contexts, to validate the theoretical developments. Such findings indeed strengthen the robustness of the whole inference procedure as well as determine the potential risks when the missingness mechanism deviates from this framework. 

Keywords

Missing at random (MAR)EM algorithmMaximum likelihood estimationInverse probability weighting (IPW)Augmented IPW (AIPW)Monte Carlo simulation

Subject Classifications

62D2062F1062F1262G0562G2062J1262F3062P10

References

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