Relevant bibliographies by topics / Linear regression

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Linear regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Linear regression"

1

Raghuvanshi, Monika. "Knowledge and Awareness: Linear Regression." Educational Process: International Journal 5, no. 4 (2016): 279–92. http://dx.doi.org/10.22521/edupij.2016.54.2.

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Lam, Kim Fung. "A Unified Linear Regression Approach." International Journal of Applied Physics and Mathematics 4, no. 4 (2014): 223–26. http://dx.doi.org/10.7763/ijapm.2014.v4.287.

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Samaniego, Angel. "CAPM-alpha estimation with robust regression vs. linear regression." Análisis Económico 38, no. 97 (2023): 27–37. http://dx.doi.org/10.24275/uam/azc/dcsh/ae/2022v38n97/samaniego.

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Genç, S., and M. Mendeş. "Multiple Linear Regression versus Automatic Linear Modelling." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 76, no. 1 (2024): 131–36. http://dx.doi.org/10.1590/1678-4162-13071.

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ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave ve
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Hersh, A., and T. B. Newman. "Linear Regression." AAP Grand Rounds 25, no. 6 (2011): 68. http://dx.doi.org/10.1542/gr.25-6-68-a.

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Pandis, Nikolaos. "Linear regression." American Journal of Orthodontics and Dentofacial Orthopedics 149, no. 3 (2016): 431–34. http://dx.doi.org/10.1016/j.ajodo.2015.11.019.

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Dombrowsky, Thomas. "Linear regression." Nursing 53, no. 9 (2023): 56–60. http://dx.doi.org/10.1097/01.nurse.0000946844.96157.68.

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Su, Xiaogang, Xin Yan, and Chih-Ling Tsai. "Linear regression." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 3 (2012): 275–94. http://dx.doi.org/10.1002/wics.1198.

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Marill, Keith A. "Advanced Statistics: Linear Regression,Part I: Simple Linear Regression." Academic Emergency Medicine 11, no. 1 (2004): 87–93. http://dx.doi.org/10.1111/j.1553-2712.2004.tb01378.x.

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Marill, Keith A. "Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression." Academic Emergency Medicine 11, no. 1 (2004): 94–102. http://dx.doi.org/10.1111/j.1553-2712.2004.tb01379.x.

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Dissertations / Theses on the topic "Linear regression"

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Bai, Xue. "Robust linear regression." Kansas State University, 2012. http://hdl.handle.net/2097/14977.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimat
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Hernandez, Erika Lyn. "Parameter Estimation in Linear-Linear Segmented Regression." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3551.pdf.

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Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.

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Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist
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Chen, Xinyu. "Inference in Constrained Linear Regression." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/405.

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Regression analyses constitutes an important part of the statistical inference and has great applications in many areas. In some applications, we strongly believe that the regression function changes monotonically with some or all of the predictor variables in a region of interest. Deriving analyses under such constraints will be an enormous task. In this work, the restricted prediction interval for the mean of the regression function is constructed when two predictors are present. I use a modified likelihood ratio test (LRT) to construct prediction intervals.
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Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.

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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonpara
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Ratnasingam, Suthakaran. "Sequential Change-point Detection in Linear Regression and Linear Quantile Regression Models Under High Dimensionality." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu159050606401363.

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Rettes, Julio Alberto Sibaja. "Robust algorithms for linear regression and locally linear embedding." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/22445.

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RETTES, Julio Alberto Sibaja. Robust algorithms for linear regression and locally linear embedding. 2017. 105 f. Dissertação (Mestrado em Ciência da Computação)- Universidade Federal do Ceará, Fortaleza, 2017.<br>Submitted by Weslayne Nunes de Sales (weslaynesales@ufc.br) on 2017-03-30T13:15:27Z No. of bitstreams: 1 2017_dis_rettesjas.pdf: 3569500 bytes, checksum: 46cedc2d9f96d0f58bcdfe3e0d975d78 (MD5)<br>Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2017-04-04T11:10:44Z (GMT) No. of bitstreams: 1 2017_dis_rettesjas.pdf: 3569500 bytes, checksum: 46cedc2d9f96d0f58bcdfe3e0
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Peraça, Maria da Graça Teixeira. "Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação." reponame:Repositório Institucional da FURG, 2009. http://repositorio.furg.br/handle/1/3436.

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Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.<br>Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)<br>Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) No. of bitstreams: 1 Modelos para estimativa do Grau
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Bocci, Cynthia Jacqueline. "Linear regression with spatially correlated data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0012/NQ52271.pdf.

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Mahmood, Nozad. "Sparse Ridge Fusion For Linear Regression." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5986.

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For a linear regression, the traditional technique deals with a case where the number of observations n more than the number of predictor variables p (n>p). In the case n<p, the classical method fails to estimate the coefficients. A solution of this problem in the case of correlated predictors is provided in this thesis. A new regularization and variable selection is proposed under the name of Sparse Ridge Fusion (SRF). In the case of highly correlated predictor , the simulated examples and a real data show that the SRF always outperforms the lasso, elastic net, and the S-Lasso, and the result
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Books on the topic "Linear regression"

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Groß, Jürgen. Linear Regression. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1.

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Olive, David J. Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1.

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John, Neter, ed. Applied linear regression models. 3rd ed. Irwin, 1996.

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Weisberg, Sanford. Applied Linear Regression. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471704091.

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Seber, George A. F. Linear regression analysis. 2nd ed. Wiley-Interscience, 2002.

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Weisberg, Sanford. Applied Linear Regression. John Wiley & Sons, Ltd., 2005.

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1946-, Lee A. J., ed. Linear regression analysis. 2nd ed. Wiley-Interscience, 2003.

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Andersen, Per Kragh, and Lene Theil Skovgaard. Regression with Linear Predictors. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7170-8.

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William, Wasserman, and Kutner Michael H, eds. Applied linear regression models. 2nd ed. Irwin, 1989.

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Kutner, Michael H. Applied linear regression models. 4th ed. McGraw-Hill, 2003.

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Book chapters on the topic "Linear regression"

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Generalized Linear Models." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_5.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian D. Marx. "Generalized Linear Models." In Regression. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63882-8_5.

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Groß, Jürgen. "Regression Diagnostics." In Linear Regression. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1_6.

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Groß, Jürgen. "Linear Admissibility." In Linear Regression. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1_4.

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Olive, David J. "Introduction." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_1.

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Olive, David J. "Multivariate Models." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_10.

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Olive, David J. "Theory for Linear Models." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_11.

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Olive, David J. "Multivariate Linear Regression." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_12.

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Olive, David J. "GLMs and GAMs." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_13.

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Olive, David J. "Stuff for Students." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_14.

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Conference papers on the topic "Linear regression"

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Jovanović, Aleksa, Aleksandra Krstić, Sanja Vujnović, and Željko Durović. "On Multivariate Linear Regression Applications." In 2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN). IEEE, 2024. http://dx.doi.org/10.1109/icetran62308.2024.10645121.

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Akhtiamov, Danil, Reza Ghane, and Babak Hassibi. "Regularized Linear Regression for Binary Classification." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619631.

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Carugno, Costantino, Maurizio Ferrari Dacrema, and Paolo Cremonesi. "Adaptive Learning for Quantum Linear Regression." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00186.

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Bisserier, A., S. Galichet, and R. Boukezzoula. "Fuzzy piecewise linear regression." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630658.

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Sweetkind-Singer, J. A. "Log-penalized linear regression." In IEEE International Symposium on Information Theory, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isit.2003.1228301.

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Wenyi Zeng and Xin Zheng. "Fuzzy Linear Regression Model." In 2008 International Symposium on Information Science and Engineering (ISISE). IEEE, 2008. http://dx.doi.org/10.1109/isise.2008.143.

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Chen, Juncheng, Jun-Sheng Ng, Nay Aung Kyaw, et al. "Incremental Linear Regression Attack." In 2022 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). IEEE, 2022. http://dx.doi.org/10.1109/asianhost56390.2022.10022167.

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Nelson, Eric, and Meir Pachter. "Linear Regression with Intercept." In AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4757.

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Li, Feiran, Kent Fujiwara, Fumio Okura, and Yasuyuki Matsushita. "Generalized Shuffled Linear Regression." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00641.

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Mandre, Ananya, Deeksha R. Hebbar, J. Shreya Rao, Ananya Keshav, Shoaib Kamal, and Trupthi Rao. "Early Forest-Fire Detection by Linear Regression, Ridge Regression And Lasso Regression." In 2023 International Conference on Computational Intelligence for Information, Security and Communication Applications (CIISCA). IEEE, 2023. http://dx.doi.org/10.1109/ciisca59740.2023.00060.

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Reports on the topic "Linear regression"

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Wallstrom, Timothy Clarke, and David Mitchell Higdon. Hierarchical Linear Regression. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1489929.

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Kubik, Harold. MLRP, Multiple Linear Regression Program. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada204565.

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Marchese, Malvina. Advanced Non-Linear Regression Modelling. Instats Inc., 2023. http://dx.doi.org/10.61700/mrtlpflhp64q7469.

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This two-day seminar offers an in-depth introduction to non-linear regression models for cross sectional data, covering binary, multinomial, ordinal, censored, count, and the very popular quantile regression models. You will learn everything you need to know in order to understand and apply these methods in your own research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point
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Marchese, Malvina. Advanced Non-Linear Regression Modelling. Instats Inc., 2023. http://dx.doi.org/10.61700/ovehw89kw8hwq469.

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This two-day seminar offers an in-depth introduction to non-linear regression models for cross sectional data, covering binary, multinomial, ordinal, censored, count, and the very popular quantile regression models. You will learn everything you need to know in order to understand and apply these methods in your own research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point
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Zarnoch, Stanley J. Testing hypotheses for differences between linear regression lines. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2009. http://dx.doi.org/10.2737/srs-rn-17.

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Zarnoch, Stanley J. Testing hypotheses for differences between linear regression lines. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2009. http://dx.doi.org/10.2737/srs-rn-17.

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DiCiccio, T. J. Likelihood Inference for Linear Regression Models. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada594293.

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Buttrey, Samuel E. The Smarter Regression" Add-In for Linear and Logistic Regression in Excel". Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada470645.

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Stock, James, and Motohiro Yogo. Testing for Weak Instruments in Linear IV Regression. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/t0284.

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Quan, Lin. Piecewise linear regression for leaf appearance rate data. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-1124.

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