Article
Multidisciplinary Sciences
Shishodiya Ghanshyam Singh, S. Vasantha Kumar
Summary: In developing countries like India, side friction in traffic, such as on-street parking and bus stops on carriageway, is a major issue impacting vehicular speed. The proposed approach of using linear combinations and ratios of independent variables effectively addresses multicollinearity issues and provides accurate speed predictions.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Statistics & Probability
Chen Lin, Kevin Wang, Samuel Mueller
Summary: This article explores traditional collinearity indices and proposes a new framework called mcvis, which uses resampling techniques to learn from these conventional indices repeatedly to better understand the causes of collinearity. It includes new collinearity measures and visualizations, particularly a bipartite plot to inform on the degree and structure of collinearity.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Computer Science, Artificial Intelligence
Chao Yang, Qiang Liu, Yi Liu, Yiu-Ming Cheung
Summary: This article proposes a novel dynamic latent variable (DLV)-based transfer learning approach, called transfer DLV regression (TDLVR), for quality prediction of multimode processes with dynamics. It can extract the dynamics between process variables and quality variables in the principal operating mode (POM) and also the co-dynamic variations among process variables between the POM and the new mode. An error compensation mechanism is incorporated to adapt to the conditional distribution discrepancy and make full use of the available labeled samples from the new mode.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Spectroscopy
Jiehong Cheng, Jun Sun, Kunshan Yao, Min Xu, Yan Cao
Summary: Feature selection plays a vital role in reducing dimensionality in the quantitative analysis of high-dimensional data. This paper proposes a variable selection method called Mutual Information-Variance Inflation Factor (MI-VIF) that combines mutual information (MI) and the variance inflation factor (VIF). By maximizing the correlation between the independent variable and the response variable and minimizing multicollinearity, MI-VIF achieves effective feature selection.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Agronomy
Franco Suarez, Cecilia Bruno, Franca Kurina Giannini, M. Paz Gimenez Pecci, Patricia Rodriguez Pardina, Monica Balzarini
Summary: This study aimed to evaluate different combinations of variable selection methods with linear and non-linear predictors to fit climate-based disease models and predict the occurrence of diseases in pathosystems. The results showed that the feature selection methods had no impact on the accuracy of predictions in the random forest algorithm, while the stepwise regression combined with VIF and p-value criteria outperformed other methods in fitting the logistic linear regression model.
EUROPEAN JOURNAL OF AGRONOMY
(2023)
Article
Statistics & Probability
Cindy Fenig, Xi Chen
Summary: In epidemiological and environmental health studies, accurately assessing the impact of multiple exposures on health outcomes is challenging. This study proposes a two-stage latent factor regression method that considers both latent factors and residual terms to address multicollinearity.
JOURNAL OF APPLIED STATISTICS
(2022)
Review
Psychology, Multidisciplinary
Selena Wang
Summary: This paper introduces latent variable network models and their integration with psychometric models, summarizing developments under network psychometrics and distinguishing graphical models under this framework from other network models. Each model is introduced using unified notations, with all methods accompanied by available R packages for further independent learning.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Statistics & Probability
Barnabe Ndabashinze, Gulesen Ustundag Siray
Summary: This article proposes a new method for determining the ridge parameter in ridge regression and compares ordinary ridge regression with generalized ridge regression. The findings indicate that the generalized ridge regression provides better prediction results.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Review
Statistics & Probability
Catalina Garcia Garcia, Roman Salmeron Gomez, Jose Garcia Perez
Summary: This paper proposes using the VIF criteria to select the biased ridge parameter and provides evidence of its effectiveness through Monte Carlo simulation and real-life applications.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Article
Environmental Sciences
Fengyi Bi, Ping Yu, Jian Jiao, Longran Zhou, Xiangcheng Zeng, Shuai Zhou
Summary: Aeromagnetic compensation is crucial in mineral exploration, as unmanned aerial vehicles generate maneuvering noise that negatively affects the accuracy of aeromagnetic measurement data. This study proposes an adaptive model-based method for suppressing aeromagnetic maneuvering noise, which optimizes the compensation model and suppresses the impact of multicollinearity. Experimental results show that this algorithm outperforms traditional methods, improving accuracy and robustness.
Article
Automation & Control Systems
Xiaobo Zhang, Zhanxue Wang, Li Zhou
Summary: The present study aims to address the issue of accuracy degradation in the traditional zero-dimensional variable cycle engine (VCE) model due to the multi-angle characteristics in the core-driven fan stage (CDFS). A two-dimensional model was established using the streamline curvature method, and a multi-level VCE model was developed to integrate the CDFS characteristics. The simulation results show that the proposed multi-level modeling approach is time-efficient and significantly improves optimal thrust and specific fuel consumption compared to the traditional zero-dimensional VCE model.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Statistics & Probability
Ainara Rodriguez Sanchez, Roman Salmeron Gomez, Catalina Garcia Garcia
Summary: This paper focuses on the application of the Stewart index in detecting collinearity and aims to establish thresholds for judging worrisome collinearity issues. The study also attempts to extend the application of the Stewart index for use after ridge regression estimation, which is commonly used for models with multicollinearity. Through Monte Carlo simulation, the study relates the Stewart index to the condition number to address the issue of multicollinearity effectively.
COMPUTATIONAL STATISTICS
(2021)
Article
Multidisciplinary Sciences
Irum Sajjad Dar, Sohail Chand, Maha Shabbir, B. M. Golam Kibria
Summary: This article proposes a new ridge regression estimator that addresses the issue of multicollinearity among independent variables. The new estimator combines the condition index, number of predictors, and error variance to automatically handle the levels of multicollinearity and signal-to-noise ratio. Extensive Monte Carlo simulations demonstrate that the proposed estimator outperforms closely related estimators in terms of minimum mean squared error (MSE). Additionally, two real-life applications are provided.
KUWAIT JOURNAL OF SCIENCE
(2023)
Article
Engineering, Civil
Arash Tavakoli, Steven Boker, Arsalan Heydarian
Summary: Analyzing the impact of the environment on drivers' stress level and workload is crucial for enhancing driving safety. This study proposes a latent-variable state-space modeling framework to estimate drivers' stress level and workload using multimodal human sensing data. The results show that external contextual elements and individual differences affect drivers' stress level and workload, and previous latent states are highly associated with current states.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Mathematics
Yongxia Zhang, Qi Wang, Maozai Tian
Summary: This paper investigates variable selection for a dataset with heavy-tailed distribution and high correlations within blocks of covariates. By introducing a latent factor model and a consistency strategy named Farvsqr, the study successfully addresses the challenges of high-dimensional data and highly correlated covariates.
