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Research ArticleOpen accessIn the keywordsLeast Absolute Deviation Regression and Least Squares for Modeling Retention Indices of Set Compounds Food and Pollutants of the EnvironmentFatiha Mebarki, Khadija Amirat, Salima Ali Mokhnach, Djellol MessadiConsidering the importance of the statistical analysis of regression in modeling based separately on study for Quantitative structure retention indices on Carbowax 20 M (ICw20M) and OV-101 columns (IOV-101) relationships (QSRR) are determined for 114 pyrazines. The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied. Least Absolute Deviation regression … …American Journal of Applied Sciences, Vol. 14, Issue 5 · 5 Jun 2017 · 7,424 accessesView PDFCite
Research ArticleOpen accessIn the abstractEcological Function Evaluation of Soil and Water Conservation Measures for Power Transmission and Transformation Project Construction Based on Remote Sensing and it’s Improvement PathYang Han, Songsong Wang, Yanhui Yang et al.Assessing ecological functions is critical for determining the efficacy of soil and water conservation measures, especially during the development of power transmission and transformation projects. Efficient conservation practices are essential for reducing ecological effects and guaranteeing sustainable development. This research aims to create a resilient model, Ecological Function Prediction for Conservation Effectiveness (EFP-CE) that will classify conservation effectiveness as either … …American Journal of Engineering and Applied Sciences, Vol. 18, Issue 1 · 17 Mar 2025 · 7,725 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimizing Mobile Edge Computing Efficiency through Multidimensional Generative Adversarial NetworkAnitha Premkumar, Sowmya Vemagal Lakshminarayana, Shankar Rajagopal et al.Big data Processing and the IoT have recently gained popularity as study topics in MEC and thorough research is sought for well-informed decisions. Mobile edge equipment, such as base stations, routers, and edge servers, usually have limited processing capabilities compared to traditional data centers. According to this perspective, this research constructs MEC using Intelligent Outlier Detection with a Multidimensional Generative … …Journal of Computer Science, Vol. 21, Issue 2 · 18 Jan 2025 · 4,686 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Adaptive Data Preprocessing Framework for Improved Learning: A Case Study of Tangier Container TerminalMostafa Al Uahabi, Hicham Attariuas, Mohammed Saleh, Mohamed ChentoufContainer terminals are critical nodes within the maritime transportation system that have a vital function in global merchandise trade, handling a significant volume of cargo through the use of various equipment and personnel. Thus, the efficiency of container terminal operations relies heavily on the ability to collect, analyze, and utilize operational data. However, such data can be corrupted by noise, … …Journal of Computer Science, Vol. 20, Issue 3 · 25 Jan 2024 · 5,585 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Collinearity Indices for Linear Models in Agricultural TrialsDanny Villegas Rivas, José M. Palacios Sánchez, Cristina A. Alzamora Rivero et al.The deleterious consequences of collinearity in linear regression on the precision of estimators of regression coefficients and the interpretability of the fitted model are widely recognized. In this study, we compare several methodologies for assessing collinearity in linear models and explore the effect of outliers on collinearity. The robustness of collinearity measures (individual and overall) is validated through two detailed … …OnLine Journal of Biological Sciences, Vol. 24, Issue 2 · 28 Dec 2023 · 6,491 accessesView PDFCite
Research ArticleOpen accessIn the abstractQuantile Regression Estimation Using Non-Crossing ConstraintsIlaria Lucrezia AmeriseIn this article we are concerned with a collection of multiple linear regressions that enable the researcher to gain an impression of the entire conditional distribution of a response variable given a set of explanatory variables. More specifically, we investigate the advantage of using a new method to estimate a bunch of non-crossing quantile regressions hyperplanes. The main tool is … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 15 May 2018 · 7,700 accessesView PDFCite
Research ArticleOpen accessIn the abstractBiases from Poor Data AnalysesTshepo Matsose, Solly Matshonisa SeeletseNon-statisticians with little knowledge in basic descriptive statistics tend to think that statistics field is limited to the content to which they are exposed. Many of them believe that a statistical package can augment the little Statistics knowledge they have. They often have a tendency to perform their own data analyses and do not even bounce it against Statistics experts … …American Journal of Applied Sciences, Vol. 13, Issue 10 · 5 Oct 2016 · 7,343 accessesView PDFCite
Research ArticleOpen accessIn the abstractA New Proposed Statistically-Derived Compromise Cut-off CD4 Count Value for HIV Patients to Start using ARVsMaria Mokgadi Lekganyane, Solly Matshonisa SeeletseThe study investigated the relationship between statistical outliers and the inconsistent values of the CD4 count recommended for starting the Antiretroviral Therapy (ART) by HIV-positive patients. Low CD4 counts imply a low immune system. It could be due to AIDS existence or closeness to death. An effective treatment to curb HIV impact is ART, which is recommended for low CD4 … …American Journal of Applied Sciences, Vol. 13, Issue 7 · 29 Jun 2016 · 7,333 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Evolving Autoregressive Predictor for Time Series ForecastingDe Z. Li, Wilson Wang, Fathy IsmailAutoregressive (AR) model is a common predictor that has been extensively used for time series forecasting. Many training methods can used to update AR model parameters, for instance, least square estimate and maximum likelihood estimate; however, both techniques are sensitive to noisy samples and outliers. To deal with the problems, an evolving AR predictor, EAR, is developed in this study … …American Journal of Engineering and Applied Sciences, Vol. 8, Issue 1 · 8 Apr 2015 · 6,965 accessesView PDFCite
Research ArticleOpen accessIn the abstractMODEL SELECTION VIA ROBUST VERSION OF R-SQUAREDShokrya SalehR-squared (R2) is a popular method for variable selection in linear regression models. R2 based on Least Squares (LS) regression minimizes the sum of the squared residuals; LS is sensitive to outlier observation. Alternative criterion based on M-estimators, which is less sensitive to outlying observation has been proposed. In this study explicit expression for such criterion is obtained when the … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 9 Oct 2014 · 7,460 accessesView PDFCite