Relevant bibliographies by topics / Stepwise regressions

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stepwise regressions'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Stepwise regressions"

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Chang, Shao-Tung, Kang-Ping Lu, and Miin-Shen Yang. "Stepwise possibilistic c-regressions." Information Sciences 334-335 (March 2016): 307–22. http://dx.doi.org/10.1016/j.ins.2015.11.042.

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Hauck, Walter W., and Rei Miike. "A proposal for examining and reporting stepwise regressions." Statistics in Medicine 10, no. 5 (May 1991): 711–15. http://dx.doi.org/10.1002/sim.4780100505.

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Streiner, David L. "Regression in the Service of the Superego: The Do's and Don'ts of Stepwise Multiple Regression." Canadian Journal of Psychiatry 39, no. 4 (May 1994): 191–96. http://dx.doi.org/10.1177/070674379403900401.

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Stepwise multiple regression is a very powerful but often misused technique. It can be used to find a set of independent variables which can predict some outcome. However, there are problems when the results of a stepwise solution are used to try to explain or understand the dependent variable. This paper discusses the different types of stepwise regressions, some of the legitimate and illegitimate uses of this technique, some of the difficulties encountered when trying to interpret the results and other solutions to the problems posed by using them.
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Ray, Supratim, Chandana Sengupta, and Kunal Roy. "QSAR modeling for lipid peroxidation inhibition potential of flavonoids using topological and structural parameters." Open Chemistry 6, no. 2 (June 1, 2008): 267–76. http://dx.doi.org/10.2478/s11532-008-0014-7.

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AbstractIn the present study, Quantitative Structure-Activity Relationship (QSAR) modeling has been carried out for lipid peroxidation (LPO)-inhibition potential of a set of 27 flavonoids, using structural and topological parameters. For the development of models, three methods were used: (1) stepwise regression, (2) factor analysis followed by multiple linear regressions (FA-MLR) and (3) partial least squares (PLS) analysis. The best equation was obtained from stepwise regression analysis (Q2 = 0.626) considering the leave-oneout prediction statistics.
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Guidolin, Massimo, and Manuela Pedio. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns." Forecasting 4, no. 1 (February 18, 2022): 275–306. http://dx.doi.org/10.3390/forecast4010016.

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In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts.
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Ray, Supratim, Chandana Sengupta, and Kunal Roy. "QSAR modeling of antiradical and antioxidant activities of flavonoids using electrotopological state (E-State) atom parameters." Open Chemistry 5, no. 4 (December 1, 2007): 1094–113. http://dx.doi.org/10.2478/s11532-007-0047-3.

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AbstractIn the present paper QSAR modeling using electrotopological state atom (E-state) parameters has been attempted to determine the antiradical and the antioxidant activities of flavonoids in two model systems reported by Burda et al. (2001). The antiradical property of a methanolic solution of 1, 1-diphenyl-2-picrylhydrazyl (DPPH) and the antioxidant activity of flavonoids in a β-carotenelinoleic acid were the two model systems studied. Different statistical tools used in this communication are stepwise regression analysis, multiple linear regressions with factor analysis as the preprocessing step for variable selection (FA-MLR) and partial least squares analysis (PLS). In both the activities the best equation is obtained from stepwise regression analysis, considering, both equation statistics and predictive ability (antiradical activity: R 2 = 0.927, Q2 = 0.871 and antioxidant activity: R 2 = 0.901, Q2 = 0.841).
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Hornby, T. George, Christopher E. Henderson, Carey L. Holleran, Linda Lovell, Elliot J. Roth, and Jeong Hoon Jang. "Stepwise Regression and Latent Profile Analyses of Locomotor Outcomes Poststroke." Stroke 51, no. 10 (October 2020): 3074–82. http://dx.doi.org/10.1161/strokeaha.120.031065.

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Background and Purpose: Previous data suggest patient demographics and clinical presentation are primary predictors of motor recovery poststroke, with minimal contributions of physical interventions. Other studies indicate consistent associations between the amount and intensity of stepping practice with locomotor outcomes. The goal of this study was to determine the relative contributions of these combined variables to locomotor outcomes poststroke across a range of patient demographics and baseline function. Methods: Data were pooled from 3 separate trials evaluating the efficacy of high-intensity training, low-intensity training, and conventional interventions. Demographics, clinical characteristics, and training activities from 144 participants >1-month poststroke were included in stepwise regression analyses to determine their relative contributions to locomotor outcomes. Subsequent latent profile analyses evaluated differences in classes of participants based on their responses to interventions. Results: Stepwise regressions indicate primary contributions of stepping activity on locomotor outcomes, with additional influences of age, duration poststroke, and baseline function. Latent profile analyses revealed 2 main classes of outcomes, with the largest gains in those who received high-intensity training and achieved the greatest amounts of stepping practice. Regression and latent profile analyses of only high-intensity training participants indicated age, baseline function, and training activities were primary determinants of locomotor gains. Participants with the smallest gains were older (≈60 years), presented with slower gait speeds (<0.40 m/s), and performed 600 to 1000 less steps/session. Conclusions: Regression and cluster analyses reveal primary contributions of training interventions on mobility outcomes in patients >1-month poststroke. Age, duration poststroke, and baseline impairments were secondary predictors. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02507466 and NCT01789853.
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Sapkota, Kamal Raj. "Study on QSPR Method for Theoretical Calculation of Boiling Point of Some organic Compounds." Himalayan Physics 3 (January 1, 2013): 93–95. http://dx.doi.org/10.3126/hj.v3i0.7316.

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Quantitative structure-property relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of boiling point using a set of 25 organic compounds. The molecular descriptors used to represent molecular structure include topological indices and constitutional descriptors. Forward stepwise regression was used to construct the QSPR models. Multiple linear regressions is utilized to construct the linear prediction model. The prediction result agrees well with the experimental value of these properties.The Himalayan PhysicsVol. 3, No. 3, July 2012Page: 93-95
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Zhang, Ying Chen, Zheng Feng Zhu, Hong Yan Wu, and Yi Ping Qiu. "Optimization Fabrication of Plasma Treated Nano-Titanium Dioxide Particles/PP/PLA Composites Filaments Using Melt Spinning." Materials Science Forum 658 (July 2010): 467–70. http://dx.doi.org/10.4028/www.scientific.net/msf.658.467.

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The Melt spinning technique of Nano-titanium dioxide particles/PP/PLA composites filaments fabricated by plasma treated Nano-titanium dioxide particles studied in the present investigation. The experimental results showed that the physical and mechanical properties of Nano-titanium dioxide particles /PP/PLA composites filaments depended on the following factors of the percentage of Nano-titanium dioxide particles, the percentage of maleic anhydride (MAH), plasma treatment parameters: flow rate of helium gas, output power, sample treatment or stationary time and flow rate of oxygen gas. Among it the flow rate of oxygen gas had the significant influence on the filament properties of tensile modulus, the yield strength and the melting temperature. Stepwise multiple regressions orthogonal design method of system optimization used to determine the percent of contribution of each factor. It found that the three indicators were different by these analyses. The experimental results indicated that the optimized conditions by stepwise multiple regressions were better than that by traditional analysis.
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Zhang, Ying Chen, Hong Yan Wu, and Y. P. Qiu. "Fabrication of Plasma Treated Nano Zinc Oxide/PP/PLA Composites Filaments Using Melt Spinning." Materials Science Forum 610-613 (January 2009): 323–34. http://dx.doi.org/10.4028/www.scientific.net/msf.610-613.323.

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The Melt spinning technique of Nano Zinc Oxide/PP/PLA composites filaments fabricated by plasma treated Nano Zinc Oxide（NZOP） was studied in the present investigation. The experimental results showed that the physical and mechanical properties of Nano Zinc Oxide/PP/PLA composites filaments depended on the following factors of the percentage of NZOP, the percentage of maleic anhydride (MAH), the plasma treatment parameters, helium gas flow rate, output power, sample treatment or stationary time and oxygen gas flow rate. Among them the oxygen gas flow rate had the significant influence on the filament properties of tensile modulus, the yield strength, the melting temperature and the crystallization temperature. Stepwise multiple regressions orthogonal design method of system optimization was used to determine the percent of contribution of each factor. It was found that the four indicators were different by these analyses. The experimental results indicated that the optimized conditions by stepwise multiple regressions were better than that by traditional analysis.
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Dissertations / Theses on the topic "Stepwise regressions"

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Jacobs, Mary Christine. "Regression Trees Versus Stepwise Regression." UNF Digital Commons, 1992. http://digitalcommons.unf.edu/etd/145.

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Many methods have been developed to determine the "appropriate" subset of independent variables in a multiple variable problem. Some of the methods are application specific while others have a wide range of uses. This study compares two such methods, Regression Trees and Stepwise Regression. A simulation using a known distribution is used for the comparison. In 699 out of 742 cases the Regression Tree method gave better predictors than the Stepwise Regression procedure.
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Petrovič, Branislav. "Regresní metody odhadu vybraných charakteristik tavených sýrů v závislosti na poměru tavicích solí." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-230946.

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This thesis deals with regression analysis of experimentally measured data of processed cheese. There is a polynomial regression used. The choice of regressors is based on Stepwise Regression and Mallows's Statistics. The estimation of the mean value is used to find the best mixture of the emulsifying salts with regards to the observed characteristic of the processed cheese. Analysis of the experiment and its results are well documented graphically.
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Emfevid, Lovisa, and Hampus Nyquist. "Financial Risk Profiling using Logistic Regression." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229821.

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As automation in the financial service industry continues to advance, online investment advice has emerged as an exciting new field. Vital to the accuracy of such service is the determination of the individual investors’ ability to bear financial risk. To do so, the statistical method of logistic regression is used. The aim of this thesis is to identify factors which are significant in determining a financial risk profile of a retail investor. In other words, the study seeks to map out the relationship between several socioeconomic- and psychometric variables to develop a predictive model able to determine the risk profile. The analysis is based on survey data from respondents living in Sweden. The main findings are that variables such as income, consumption rate, experience of a financial bear market, and various psychometric variables are significant in determining a financial risk profile.
I samband med en ökad automatiseringstrend har digital investeringsrådgivning dykt upp som ett nytt fenomen. Av central betydelse är tjänstens förmåga att bedöma en investerares förmåga till att bära finansiell risk. Logistik regression tillämpas för att bedöma en icke- professionell investerares vilja att bära finansiell risk. Målet med uppsatsen är således att identifiera ett antal faktorer med signifikant förmåga till att bedöma en icke-professionell investerares riskprofil. Med andra ord, så syftar denna uppsats till att studera förmågan hos ett antal socioekonomiska- och psykometriska variabler. För att därigenom utveckla en prediktiv modell som kan skatta en individs finansiella riskprofil. Analysen genomförs med hjälp av en enkätstudie hos respondenter bosatta i Sverige. Den huvudsakliga slutsatsen är att en individs inkomst, konsumtionstakt, tidigare erfarenheter av abnorma marknadsförhållanden, och diverse psykometriska komponenter besitter en betydande förmåga till att avgöra en individs finansiella risktolerans
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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
Department of Statistics
Paul Nelson
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, increasing sample size leads to a relatively small improvement in both methods and that the magnitude of the variance of the error term is the major factor determining the performance of both.
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Daghighi, Amin. "Harmful Algae Bloom Prediction Model for Western Lake Erie Using Stepwise Multiple Regression and Genetic Programming." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1502190026473106.

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Maxwell, Kori Lloyd Hugh. "Logistic Regression Analysis to Determine the Significant Factors Associated with Substance Abuse in School-Aged Children." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/67.

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Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.
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Hu, Qing. "Predictor Selection in Linear Regression: L1 regularization of a subset of parameters and Comparison of L1 regularization and stepwise selection." Link to electronic thesis, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-051107-154052/.

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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 variable or excluding variables that are important. Failing to capture the true effects would paint a picture disconnected from reality and it would also give a false impression of what reality really looks like. To mitigate this risk a simulation study has been conducted to find out what variable selection algorithms to apply in order to make more accurate inference. The different algorithms being tested are stepwise regression, backward elimination and lasso regression. Lasso performed worst when applied to a small sample but performed best when applied to larger samples. Backward elimination and stepwise regression had very similar results.
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Dawson, Amanda Caroline St Vincent???s Hospital Clinical School UNSW. "Evaluation of novel molecular markers from the WNT pathway : a stepwise regression model for pancreatic cancer survival." Awarded by:University of New South Wales. St Vincent???s Hospital Clinical School, 2007. http://handle.unsw.edu.au/1959.4/31528.

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Optimisation of the conventional tripartite of pancreatic cancer (PC) treatment have led to significant improvements in mortality, however further knowledge of the underlying molecular processes is still required. Transcript profiling of mRNA expression of over 44K genes with microarray technology demonstrated upregulation of secreted frizzled related protein 4 (sFRP4) and ??-catenin in PC compared to normal pancreata. Their pathway ??? Wnt signalling is integral to transcriptional regulation and aberrations in these molecules are critical in the development of many human malignancies. Immunohistochemistry protocols were evaluated by two independent blinded examiners for antigen expression differences associated with survival patterns in 140 patients with biopsy verified PC and a subset of 23 normal pancreata with substantial observer agreement (kappa value 0.6-0.8). A retrospective cohort was identified from 6 Sydney hospitals between 1972-2003 and archival formalin fixed tissue was collected together with clinicopathological data. Three manual stepwise regression models were fitted for overall, disease-specific and relapse-free survival to determine the value of significant prognostic variables in risk stratification. The models were fitted in a logical order using a careful strategy with step by step interpretation of the results. Immunohistochemistry demonstrated increased sFRP4 membranous expression (> 10%) in 49/95 PC specimens and this correlated with improved overall survival (HR:0.99;95%CI:0.97-6.40;LRchi2=134.75; 1df; ??< 0.001). Increased sFRP4 cytoplasmic staining (> 2/3) in 46/85 patients increased the disease-specific survival (HR:0.52;95%CI:0.31-0.89;LR test statistic =248.40;1df;??< 0.001). Increasing ??-catenin membranous expression (< _60%) in 26/116 patients was associated with an increased risk of overall death (HR:3.18;95%CI:1.14-8.89;LR test statistic =4.61;1df,??< 0.05). Increasing cytoplasmic expression in 65/114 patients was protective and was associated with prolonged survival on univariate, but not multivariate analysis (Disease specific survival HR:0.75;95%CI:0.56-1.00;logrank chi2=3.91;1df; ??=0.05). Increased nuclear ??-catenin expression in 65/114 patients was associated with prolonged survival (disease-specific HR:0.92;95%CI:0.83-1.02; LR test statistic= 49.72;1df;??< 0.001). At the conclusion, 12 patients (8.6%) remained alive, 122 died of their disease (68 males versus 54 females). They were followed for a median of 8.7 months (range 1.0-131.3) months. The median age was 66.5 years (range 34.4-96.0, standard deviation 10.9) years. Pancreatic resection was achieved in 79 patients with 46.8% achieving RO resection. The 30 day post-operative mortality was 2.1%. The overall 1 year survival rate was (33.7% ; 95%CI: 25.78-33.79) with a 5 year survival of (2.87%, 95%CI: 2.83-6.01) and a median survival of (8.90 months; 95%CI: 7.5-10.2). The median disease-specific survival was (9.40; 95%CI: 7.9-10.5 months) and the median time to relapse was 1.2 months (95%CI 1.0-1.2 months). A central tenet of contemporary cancer research is that an understanding of the genetic and molecular abnormalities that accompany the development and progression of cancer is critical to further advances in diagnosis, treatment and eventual prevention. High throughput tissue microarrays were used to study expression of two novel tumour markers in a cohort of pancreatic cancer patients and identified sFRP4 and ??-catenin as potential novel prognostic markers.
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Hinds, H. A. "The application of a modified stepwise regression (MSR) method to the estimation of aircraft stability and control derivatives." Thesis, Cranfield University, 1996. http://hdl.handle.net/1826/3624.

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A programme of research has now been completed in the College of Aeronautics (CoA) at Cranfield University to investigate the use of a Modified Stepwise Regression (MSR) procedure. The technique was applied to data obtained from a small BAe Hawk aircraft model flown in a dynamic wind tunnel facility in order to try to estimate the aerodynamic stability and control derivatives of the model. A variety of preliminary experiments were performed to enable the static stability of the Hawk model to be evaluated and estimates for a limited number of aerodynamic derivatives were obtained. The initial experiments also allowed data acquisition and processing systems to be developed. Experience of flying and controlling the model in the wind tunnel was gained. The MSR technique was implemented in the form of a FORTRAN 77 software program. Computer simulations of both the full scale Hawk aircraft and scaled wind tunnel model were written. MSR was found to produce perfect derivative estimates when using noise-free data produced by the aircraft simulations. Various mathematical models were produced to represent the reduced order small perturbation equations of motion for the Hawk in the wind tunnel. Different methods for re-constructing the perturbation variables were implemented. Although the MSR procedure did not perform optimally with experimental data, some insight into both the MSR method and the practical difficulties associated with using a small dynamic rig has been gained.
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Books on the topic "Stepwise regressions"

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Hinds, H. A. Third quarterly report on the application of modified stepwise regression for the estimation of aircraft stability and control parameters. Cranfield, Bedford: College of Aeronautics, Cranfield Institute of Technology, 1989.

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Cook, M. V. Initial review of research into the application of modified stepwise regression for the estimation of aircraft stability and control parameters. Cranfield, Bedford: College of Aeronautics, Cranfield Institute of Technology, 1989.

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Hinds, H. A. Review of initial experiments using the Hawk model, dynamic rig facility and the CED1401 digital data acquisition equipment. Cranfield, Bedford, England: Cranfield Institute of Technology, College of Aeronautics, 1990.

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Hinds, H. A. Measurement of the longitudinal static stability and the moments of 0inertia of a 1/12th scale model of a B.Ae Hawk. Cranfield, Bedford: College of Aeronautics, Cranfield Institute of Technology, 1990.

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On stepwise procedures for some multiple inference problems. Göteborg: Alqvist & Wiksell International, 1989.

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Hinds, H. A. Preliminary studies for aircraft parameter estimation using modified stepwise regression. Cranfield, Bedford, England: College of Aeronautics, Cranfield Institute of Technology, 1989.

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Hoff, J. C. Initial evaluation of the modified stepwise regression procedure to estimate aircraft stability and control parameters from flight test data. Cranfield, Bedford, England\: Cranfield University, College of Aeronautics, 1993.

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Billings, S. A. A prediction error and stepwise regression estimation algorithm for nonlinear systems. Sheffield: University, Dept. of Control Engineeering, 1985.

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Hinds, H. A. Second quarterly report on the application of modified stepwise regression for the estimation of aircraft stability and control parameters. Cranfield, Bedford, England: Cranfield Institute of Technology, College of Aeronautics, 1989.

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Cheng, Russell. Nested Nonlinear Regression Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0015.

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Stepwise fitting of nonlinear nested regression models is considered in this chapter. The forward stepwise method of linear model building is used as far as possible. With linear models this is straightforward as there is in principle a free choice of the order that individual terms or factors are selected for inclusion. The only real issue is that sufficient submodels are examined to ensure that those finally selected really are amongst the best. The nonlinear case is not so straightforward, as embeddedness and parameter indeterminacy issues impose restrictions on the order in which steps can be taken to build a valid model, as certain parameters can only be meaningfully included if other specific parameters are definitely present. A systematic way of building valid nonlinear models of increasing complexity is described and illustrated by two examples using real data. A brief review of non-nested model building is also given.
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Book chapters on the topic "Stepwise regressions"

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Gooch, Jan W. "Stepwise Regression." In Encyclopedic Dictionary of Polymers, 998. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15392.

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Armstrong, Richard A., and Anthony C. Hilton. "Stepwise Multiple Regression." In Statistical Analysis in Microbiology: Statnotes, 135–38. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch26.

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Pan, Jie, Shuxia Ren, Dongzhang Rao, Zongxian Zhao, and Wenshi Xue. "Stepwise Masking: A Masking Strategy Based on Stepwise Regression for Pre-training." In Natural Language Processing and Chinese Computing, 131–42. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17189-5_11.

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Bailly, Kevin, Maurice Milgram, and Philippe Phothisane. "Head Pose Estimation by a Stepwise Nonlinear Regression." In Computer Analysis of Images and Patterns, 25–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_3.

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Pandit, Vinay, and Zahid Y. Khairullah. "Stepwise Regression Choosing the Proper Level of Significance." In Proceedings of the 1985 Academy of Marketing Science (AMS) Annual Conference, 395–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16943-9_84.

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Silhavy, Petr, Radek Silhavy, and Zdenka Prokopova. "Stepwise Regression Clustering Method in Function Points Estimation." In Advances in Intelligent Systems and Computing, 333–40. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00211-4_29.

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Gupta, Anil, Jizu Cheng, and Sunil Saigal. "Stepwise Linear Regression Particular Integrals for Uncoupled Thermoelasticity with Boundary Elements." In Boundary Element Methods in Engineering, 207–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84238-2_27.

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Cong, Lingbo, and Jihua Cai. "Study on Rural Residents Income Growth Based on Quasi-Stepwise Regression Model." In Lecture Notes in Electrical Engineering, 695–703. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35470-0_85.

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Silhavy, Petr, Radek Silhavy, and Zdenka Prokopova. "Evaluation of Data Clustering for Stepwise Linear Regression on Use Case Points Estimation." In Advances in Intelligent Systems and Computing, 491–96. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57141-6_52.

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Hamid, Nor Baizura, Mohd Erwan Sanik, Hafsa Mohammad Noor, Joewono Prasetijo, Mardiha Mokhtar, Mohamad Azim Mohammad Azmi, Mohamad Irwan Yahaya, and Mohd Zakwan Ramli. "Prediction Model of Mass Rapid Transit Noise Level Using the Stepwise Regression Analysis." In Springer Proceedings in Physics, 379–89. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8903-1_33.

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Conference papers on the topic "Stepwise regressions"

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Lan, Yuqing, and Shuhang Guo. "Multiple Stepwise Regression Analysis on Knowledge Evaluation." In 2008 International Conference on Management of e-Commerce and e-Government. IEEE, 2008. http://dx.doi.org/10.1109/icmecg.2008.65.

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Feng, Keyu, and Quanbao Li. "Using stepwise regression and support vector regression to comprise REITs' portfolio." In 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2014. http://dx.doi.org/10.1109/itaic.2014.7065026.

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Nong, Jifu. "Variable Selection by Stepwise Slicing in Nonparametric Regression." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.304.

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Rasool, Raza, and Ali Afzal Malik. "Effort estimation of ETL projects using Forward Stepwise Regression." In 2015 International Conference on Emerging Technologies (ICET). IEEE, 2015. http://dx.doi.org/10.1109/icet.2015.7389209.

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Vlachopoulou, Maria, Tom Ferryman, Ning Zhou, and Jianzhong Tong. "A stepwise regression method for forecasting net interchange schedule." In 2013 IEEE Power & Energy Society General Meeting. IEEE, 2013. http://dx.doi.org/10.1109/pesmg.2013.6672763.

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Su Shi, Wang Zhe, and Wang Fei. "Estimation of solar irradiation based on multiple stepwise regression." In 2011 IEEE PES Innovative Smart Grid Technologies (ISGT Australia). IEEE, 2011. http://dx.doi.org/10.1109/isgt-asia.2011.6167101.

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Ning Zhou, J. Pierre, and D. Trudnowski. "A stepwise regression method for estimating dominant electromechanical modes." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6344635.

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JANG, IN SOCK, RODRIGO DIENSTMANN, ADAM A. MARGOLIN, and JUSTIN GUINNEY. "STEPWISE GROUP SPARSE REGRESSION (SGSR): GENE-SET-BASED PHARMACOGENOMIC PREDICTIVE MODELS WITH STEPWISE SELECTION OF FUNCTIONAL PRIORS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2014. http://dx.doi.org/10.1142/9789814644730_0005.

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Sarı, Tuğba. "Performance Evaluation of Turkish Banks with TOPSIS and Stepwise Regression." In Proceedings of The International Conference on Research in Business, Management and Finance. GLOBALKS, 2019. http://dx.doi.org/10.33422/icrbmf.2019.07.999.

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Abdelmutalab, Ameen, Khaled Assaleh, and Mohamed El-Tarhuni. "Automatic modulation classification using hierarchical polynomial classifier and stepwise regression." In 2016 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2016. http://dx.doi.org/10.1109/wcnc.2016.7565127.

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Reports on the topic "Stepwise regressions"

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Cheverud, James, Claire C. Gordon, Robert A. Walker, Cashell Jacquish, Luci Kohn, Allen Moore, and Nyuta Yamashita. Anthropometric Survey of US Army Personnel (1988): Correlation Coefficients and Regression Equations. Part 5. Stepwise and Standard Multiple Regression Tables. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada224990.

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