Relevant bibliographies by topics / Linear regression

Contents

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

Academic literature on the topic 'Linear regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Linear regression"

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Raghuvanshi, Monika. "Knowledge and Awareness: Linear Regression." Educational Process: International Journal 5, no. 4 (December 1, 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 (January 20, 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 very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.
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Hersh, A., and T. B. Newman. "Linear Regression." AAP Grand Rounds 25, no. 6 (June 1, 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 (March 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 (September 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 (February 10, 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 (January 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 (January 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
Department of Statistics
Weixin Yao
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 estimate, LTS estimate, S-estimate, [tau]-estimate, MM-estimate, GM-estimate, and REWLS estimate. Finally, we compare these robust estimates based on their robustness and efficiency through a simulation study. A real data set application is also provided to compare the robust estimates with traditional least squares estimator.
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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 with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.
Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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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 nonparametric fit. We apply this idea of model robustness in the framework of the linear mixed model. The mixed model robust regression (MMRR) predictions we propose are convex combinations of predictions obtained from a standard normal-theory linear mixed model, which serves as the parametric model component, and a locally weighted maximum likelihood fit which serves as the nonparametric component. An application of this technique with real data is provided.
Ph. D.
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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.
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Nowadays a very large quantity of data is flowing around our digital society. There is a growing interest in converting this large amount of data into valuable and useful information. Machine learning plays an essential role in the transformation of data into knowledge. However, the probability of outliers inside the data is too high to marginalize the importance of robust algorithms. To understand that, various models of outliers are studied. In this work, several robust estimators within the generalized linear model for regression framework are discussed and analyzed: namely, the M-Estimator, the S-Estimator, the MM-Estimator, the RANSAC and the Theil-Sen estimator. This choice is motivated by the necessity of examining algorithms with different working principles. In particular, the M-, S-, MM-Estimator are based on a modification of the least squares criterion, whereas the RANSAC is based on finding the smallest subset of points that guarantees a predefined model accuracy. The Theil Sen, on the other hand, uses the median of least square models to estimate. The performance of the estimators under a wide range of experimental conditions is compared and analyzed. In addition to the linear regression problem, the dimensionality reduction problem is considered. More specifically, the locally linear embedding, the principal component analysis and some robust approaches of them are treated. Motivated by giving some robustness to the LLE algorithm, the RALLE algorithm is proposed. Its main idea is to use different sizes of neighborhoods to construct the weights of the points; to achieve this, the RAPCA is executed in each set of neighbors and the risky points are discarded from the corresponding neighborhood. The performance of the LLE, the RLLE and the RALLE over some datasets is evaluated.
Na atualidade um grande volume de dados é produzido na nossa sociedade digital. Existe um crescente interesse em converter esses dados em informação útil e o aprendizado de máquinas tem um papel central nessa transformação de dados em conhecimento. Por outro lado, a probabilidade dos dados conterem outliers é muito alta para ignorar a importância dos algoritmos robustos. Para se familiarizar com isso, são estudados vários modelos de outliers. Neste trabalho, discutimos e analisamos vários estimadores robustos dentro do contexto dos modelos de regressão linear generalizados: são eles o M-Estimator, o S-Estimator, o MM-Estimator, o RANSAC e o Theil-Senestimator. A escolha dos estimadores é motivada pelo principio de explorar algoritmos com distintos conceitos de funcionamento. Em particular os estimadores M, S e MM são baseados na modificação do critério de minimização dos mínimos quadrados, enquanto que o RANSAC se fundamenta em achar o menor subconjunto que permita garantir uma acurácia predefinida ao modelo. Por outro lado o Theil-Sen usa a mediana de modelos obtidos usando mínimos quadradosno processo de estimação. O desempenho dos estimadores em uma ampla gama de condições experimentais é comparado e analisado. Além do problema de regressão linear, considera-se o problema de redução da dimensionalidade. Especificamente, são tratados o Locally Linear Embedding, o Principal ComponentAnalysis e outras abordagens robustas destes. É proposto um método denominado RALLE com a motivação de prover de robustez ao algoritmo de LLE. A ideia principal é usar vizinhanças de tamanhos variáveis para construir os pesos dos pontos; para fazer isto possível, o RAPCA é executado em cada grupo de vizinhos e os pontos sob risco são descartados da vizinhança correspondente. É feita uma avaliação do desempenho do LLE, do RLLE e do RALLE sobre algumas bases de dados.
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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.
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Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto.
In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data can be measured and it can be verified that the most influent variables affecting the SD are the maximum temperature and the relative humidity. After verifying this result, statistical models were developed aiming to calculate, based on the environmental data provided by the meteorological database and without waste of time and money, the approximate averages of SD for each seasonal station of the south region of Brazil, thus providing a better estimative of life-cycle for concrete structures.
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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 nM.S.
Masters
Statistics
Sciences
Statistical Computing
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Books on the topic "Linear regression"

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

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

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

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

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

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Applied linear regression. 3rd ed. Hoboken, N.J: Wiley-Interscience, 2005.

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Applied linear regression. 2nd ed. New York: Wiley, 1985.

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

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

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Regression and linear models. New York: McGraw-Hill, 1990.

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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, 269–324. Berlin, Heidelberg: 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, 283–342. Berlin, Heidelberg: 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, 293–329. Berlin, Heidelberg: 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, 213–56. Berlin, Heidelberg: 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, 1–15. Cham: 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, 299–312. Cham: 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, 313–42. Cham: 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, 343–87. Cham: 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, 389–458. Cham: 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, 459–71. Cham: 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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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, Zhili Zou, Kwen-Siong Chong, Zhiping Lin, and Bah-Hwee Gwee. "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. Reston, Virigina: 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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Goetschalckx, Robby, Kurt Driessens, and Scott Sanner. "Cost-Sensitive Parsimonious Linear Regression." In 2008 Eighth IEEE International Conference on Data Mining (ICDM). IEEE, 2008. http://dx.doi.org/10.1109/icdm.2008.76.

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Chahad, Abdelkader, Ali Laksaci, and Ait-Hennani Larbi. "Functional local linear relative regression." In 2020 2nd International Conference on Mathematics and Information Technology (ICMIT). IEEE, 2020. http://dx.doi.org/10.1109/icmit47780.2020.9047027.

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Lemos, Andre, Walmir Caminhas, and Fernando Gomide. "Evolving fuzzy linear regression trees." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5583970.

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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), January 2019. http://dx.doi.org/10.2172/1489929.

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Kubik, Harold. MLRP, Multiple Linear Regression Program. Fort Belvoir, VA: Defense Technical Information Center, July 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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4

Marchese, Malvina. Advanced Non-Linear Regression Modelling. Instats Inc., 2023. http://dx.doi.org/10.61700/ovehw89kw8hwq469.

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Abstract:
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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5

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

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7

DiCiccio, T. J. Likelihood Inference for Linear Regression Models. Fort Belvoir, VA: Defense Technical Information Center, November 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". Fort Belvoir, VA: Defense Technical Information Center, July 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. Cambridge, MA: National Bureau of Economic Research, November 2002. http://dx.doi.org/10.3386/t0284.

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Graham, Bryan, and Cristine Campos de Xavier Pinto. Semiparametrically Efficient Estimation of the Average Linear Regression Function. Cambridge, MA: National Bureau of Economic Research, November 2018. http://dx.doi.org/10.3386/w25234.

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