Relevant bibliographies by topics / Backward stepwise elimination

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Backward stepwise elimination'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Backward stepwise elimination"

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Henderson, Douglas A., and Daniel R. Denison. "Stepwise Regression in Social and Psychological Research." Psychological Reports 64, no. 1 (1989): 251–57. http://dx.doi.org/10.2466/pr0.1989.64.1.251.

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Researchers often invoke stepwise ordinary least squares regression to explain, predict or classify practical problems or theoretical constructs in psychological and social research. Unfortunately, this statistical technique is used without proper consideration for its inherent theoretical and practical limitations, a problem expected to grow even more serious with the proliferation of statistical packages for use on personal computers. Use of stepwise regression in social and psychological research is reconsidered here. Explanations of forward selection, backward elimination and combination s
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Rahman, Md Siddiqur, and Jafar A. Khan. "Building a Robust Linear Model with Backward Elimination Procedure." Dhaka University Journal of Science 62, no. 2 (2015): 87–93. http://dx.doi.org/10.3329/dujs.v62i2.21971.

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For building a linear prediction model, Backward Elimination (BE) is a computationally suitable stepwise procedure for sequencing the candidate predictors. This method yields poor results when data contain outliers and other contaminations. Robust model selection procedures, on the other hand, are not computationally efficient or scalable to large dimensions, because they require the fitting of a large number of submodels. Robust version of BE is proposed in this study, which is computationally very suitable and scalable to large high-dimensional data sets. Since BE can be expressed in terms o
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A., Ademuyiwa J., and Adetunji A. A. "Impact of Some Economic Variables on the Real Gross Domestic Product of Nigeria." Budapest International Research and Critics Institute (BIRCI-Journal) : Humanities and Social Sciences 2, no. 4 (2019): 12–19. http://dx.doi.org/10.33258/birci.v2i4.563.

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The influences of External Debt Service (EDS), External Debt Stock (EDSt), Government Expenditure (GE), Inflation Rate (InfR), Interest Rate (IntR) and Exchange Rate (ExR) of Nigeria on the Real Gross Domestic Product (RGDP) are examined. Results of the analysis using Stepwise Regression (Backward Elimination and Forward Selection) reveals that GE, EDS, and IntR have positive significant contributions to the RGDP of the country compared to other variables considered.
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Hsu, Fang-Chen, Chien-Nan Chen, and Meng-Dar Shieh. "Using stepwise backward elimination to specify terms related to tactile sense for product design." Advanced Engineering Informatics 46 (October 2020): 101193. http://dx.doi.org/10.1016/j.aei.2020.101193.

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Joseph, Shijo, K. Anitha, V. K. Srivastava, Ch Sudhakar Reddy, A. P. Thomas, and M. S. R. Murthy. "Rainfall and Elevation Influence the Local-Scale Distribution of Tree Community in the Southern Region of Western Ghats Biodiversity Hotspot (India)." International Journal of Forestry Research 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/576502.

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The present study characterises the tree communities with respect to topographic and climatic variables and identifies the most important environmental correlate of species richness in the southern region of Western Ghats Biodiversity Hotspot, India. Digitally derived environmental variables in combination with tree species richness information were analysed using Canonical Correspondence Analysis (CCA) to characterise the communities. Multiple regression technique based on stepwise backward elimination was used to identify the most important environment correlate of species richness. Canonica
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Untadi, Albertus, Lily D. Li, Michael Li, and Roland Dodd. "Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion." Axioms 12, no. 6 (2023): 524. http://dx.doi.org/10.3390/axioms12060524.

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Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model’s forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applie
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Hamdan, Khaled. "Model for Predicting Corrosion Under Insulation Using Nondestructive Testing." Materials Performance 61, no. 4 (2022): 52–56. https://doi.org/10.5006/mp2022_61_4-52.

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The objective of this article is to present the model for predicting a corrosion under insulation rate by using several field nondestructive testing (NDT) inspection data, including stress-strain profile, surface temperature profile, and moisture content of insulation. The backward stepwise elimination regression method was utilized to find the influential factor for the model. Prior to the algorithm selection, the unsupervised learning analysis was performed to determine the best algorithm for the model. The algorithm used for the final model was an artificial neural network that utilizes the
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Ullmann, Theresa, Georg Heinze, Lorena Hafermann, Christine Schilhart-Wallisch, and Daniela Dunkler. "Evaluating variable selection methods for multivariable regression models: A simulation study protocol." PLOS ONE 19, no. 8 (2024): e0308543. http://dx.doi.org/10.1371/journal.pone.0308543.

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Researchers often perform data-driven variable selection when modeling the associations between an outcome and multiple independent variables in regression analysis. Variable selection may improve the interpretability, parsimony and/or predictive accuracy of a model. Yet variable selection can also have negative consequences, such as false exclusion of important variables or inclusion of noise variables, biased estimation of regression coefficients, underestimated standard errors and invalid confidence intervals, as well as model instability. While the potential advantages and disadvantages of
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Azizah, Siti Nur, Dela Gustiara, Anwar Fitrianto, Erfiani, and Pika Silvianti. "IDENTIFICATION OF PRIMARY SCHOOL LITERACY ACHIEVEMENT FACTORS IN PROVINCE X USING ORDINAL STEPWISE LOGISTIC." Jurnal Statistika dan Aplikasinya 9, no. 1 (2025): 22–36. https://doi.org/10.21009/jsa.09103.

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Literacy is a foundational skill that underpins students’ academic success and lifelong opportunities. Low literacy skills can result in long-term disadvantages such as limited access to higher education, low productivity, and social inequality. Indonesia continues to face challenges in improving students' literacy achievement, particularly at the primary school level. According to the PISA 2022 results, Indonesia ranked 69th out of 81 countries, indicating that students’ literacy levels remain relatively low. This study aims to identify the factors that influence the literacy achievement of p
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Badshah, Waqar, and Mehmet Bulut. "Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence." Economies 8, no. 2 (2020): 49. http://dx.doi.org/10.3390/economies8020049.

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Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwis
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Dissertations / Theses on the topic "Backward stepwise elimination"

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Li, Xin. "A simulation evaluation of backward elimination and stepwise variable selection in regression analysis." Kansas State University, 2012. http://hdl.handle.net/2097/14094.

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Master of Science<br>Department of Statistics<br>Paul Nelson<br>A first step in model building in regression analysis often consists of selecting a parsimonious set of independent variables from a pool of candidate independent variables. This report uses simulation to study and compare the performance of two widely used sequential, variable selection algorithms, stepwise and backward elimination. A score is developed to assess the ability of any variable selection method to terminate with the correct model. It is found that backward elimination performs slightly better than stepwise, increasin
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SINGH, KEVIN. "Comparing Variable Selection Algorithms On Logistic Regression – A Simulation." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446090.

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When we try to understand why some schools perform worse than others, if Covid-19 has struck harder on some demographics or whether income correlates with increased happiness, we may turn to regression to better understand how these variables are correlated. To capture the true relationship between variables we may use variable selection methods in order to ensure that the variables which have an actual effect have been included in the model. Choosing the right model for variable selection is vital. Without it there is a risk of including variables which have little to do with the dependent va
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Book chapters on the topic "Backward stepwise elimination"

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Dhamodharavadhani S. and Rathipriya R. "Variable Selection Method for Regression Models Using Computational Intelligence Techniques." In Handbook of Research on Machine and Deep Learning Applications for Cyber Security. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9611-0.ch019.

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Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.
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Dhamodharavadhani S. and Rathipriya R. "Variable Selection Method for Regression Models Using Computational Intelligence Techniques." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch037.

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Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.
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Conference papers on the topic "Backward stepwise elimination"

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Kidman, David, Craig Stevens, Todd Remund, and William Kitto. "Model Selection Made Easy Using Information Theoretics: An Aircraft Propulsion System Modeling Problem." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25359.

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The United States Department of Defense (DoD) is continually looking for ways to improve test and evaluation techniques to ensure systems meet military requirements prior to acquisition. Recently, the DoD has been pursuing the use of statistical methods to improve test and evaluation. This paper highlights statistical methodologies used by the Air Force Test Center to improve aircraft propulsion system Modeling and Simulation (M&amp;S) efforts. The US Air Force has a long history of using M&amp;S (more than 55 years) during aircraft test and evaluation. In the past, M&amp;S usage was primarily
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