Nonlinear set membership filter with state estimation constraints via consensus-ADMM

Li, Xiaowei; Zhang, Xuqi; Wang, Zhiguo; Shen, Xiaojing

doi:10.1016/j.automatica.2022.110842

Cited by 2 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early literature on the estimation problem with state constraints includes [51] and [52]. Recently, some scholars have investigated the incorporation of different constraint conditions into target state updates in the set-valued framework, proposing set-valued filters and algorithms for constrained state estimation in the presence of unknown but bounded noise, yielding promising results [53], [54]. These studies mainly address set-valued filtering for constrained dynamic systems with unknown but bounded noise, contributing to improved reliability and robustness of state models in engineering applications.…”

Section: Several Key Results Have Been Achieved In the Context Of Linearmentioning

confidence: 99%

Regression with Set-Valued Categorical Predictors

Wang¹,

Ding²,

Yang³

2024

STAT SINICA

View full text Add to dashboard Cite

We address the regression problem with a new form of data that arises from data privacy applications. Instead of point values, the observed explanatory variables are subsets containing each individual's original value. The classical regression analyses such as least squares are not applicable since the set-valued predictors only carry partial information about the original values. We propose a computationally efficient subset least squares method to perform regression for such data. We establish upper bounds of the prediction loss and risk in terms of the subset structure, the model structure, and the data dimension.The error rates are shown to be optimal under some common situations. Furthermore, we develop a model selection method to identify the most appropriate model for prediction.Experiment results on both simulated and real-world datasets demonstrate the promising performance of the proposed method.

show abstract