Uncertain logistic and Box-Cox regression analysis with maximum likelihood estimation

Liang Fang, Yiping Hong, Zaiying Zhou, Wenhui Chen

Research output: Contribution to journalArticlepeer-review


Although the maximum likelihood estimation (MLE) for the uncertain discrete models has long been an academic interest, it has yet to be proposed in the literature. Thus, this study proposes the uncertain MLE for discrete models in the framework of the uncertainty theory, such as the uncertain logistic regression model. We also generalize the estimation proposed by Lio and Liu and obtain the uncertain MLE for non-linear continuous models, such as the uncertain Box-Cox regression model. Our proposed methods provide a useful tool for making inferences regarding non-linear data that is precisely or imprecisely observed, especially data based on degrees of belief, such as an expert’s experimental data. We demonstrate our methodology by calculating proposed estimates and providing forecast values and confidence intervals for numerical examples. Moreover, we evaluate our proposed models via residual analysis and the cross-validation method. The study enriches the definition of the uncertain MLE, thus making it easy to construct estimation and prediction methods for general uncertainty models.
Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalCommunications in Statistics - Theory and Methods
StatePublished - Sep 12 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-15
Acknowledgements: This work was supported in part by the Beijing Forestry University Education and Teaching Research Project under Grant [2020KCSZ062, BJFU2020JY023] and the Fundamental Research Funds for the Central Universities under Grant [BLX2019446].

ASJC Scopus subject areas

  • Statistics and Probability


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