Relevant bibliographies by topics / Regresión de Ridge

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

Academic literature on the topic 'Regresión de Ridge'

Author: Grafiati
Published: 17 September 2021
Last updated: 18 February 2022

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Journal articles on the topic "Regresión de Ridge"

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Pérez-Planells, Ll, J. Delegido, J. P. Rivera-Caicedo, and J. Verrelst. "Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos." Revista de Teledetección, no. 44 (December 22, 2015): 55. http://dx.doi.org/10.4995/raet.2015.4153.

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<p class="Bodytext">Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.</p>
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McDonald, Gary C. "Ridge regression." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 1 (July 2009): 93–100. http://dx.doi.org/10.1002/wics.14.

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Fearn, Tom. "Ridge Regression." NIR news 24, no. 3 (May 2013): 18–19. http://dx.doi.org/10.1255/nirn.1365.

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Rashid, Khasraw A., and Hanaw A. Amin. "Ridge Estimates of Regression Coefficients for SoilMoisture Retention of IraqiSoils." Journal of Zankoy Sulaimani - Part A 18, no. 3 (February 25, 2016): 85–98. http://dx.doi.org/10.17656/jzs.10537.

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Dorugade, A. V. "New ridge parameters for ridge regression." Journal of the Association of Arab Universities for Basic and Applied Sciences 15, no. 1 (April 2014): 94–99. http://dx.doi.org/10.1016/j.jaubas.2013.03.005.

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Munroe, Jeffrey S. "Ground Penetrating Radar Investigation of Late Pleistocene Shorelines of Pluvial Lake Clover, Elko County, Nevada, USA." Quaternary 3, no. 1 (March 20, 2020): 9. http://dx.doi.org/10.3390/quat3010009.

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Beach ridges constructed by pluvial Lake Clover in Elko County, Nevada during the Late Pleistocene were investigated with ground-penetrating radar (GPR). The primary objective was to document the internal architecture of these shorelines and to evaluate whether they were constructed during lake rise or fall. GPR data were collected with a ground-coupled 400-Mhz antenna and SIR-3000 controller. To constrain the morphology of the ridges, detailed topographic surveys were collected with a Topcon GTS-235W total station referenced to a second class 0 vertical survey point. GPR transects crossed the beach ridge built by Lake Clover at its highstand of 1725 m, along with seven other ridges down to the lowest beach at 1712 m. An average dielectric permittivity of 5.0, typical for dry sand and gravel, was calculated from GPR surveys in the vicinity of hand-excavations that encountered prominent stratigraphic discontinuities at known depths. Assuming this value, consistent radar signals were returned to a depth of ~3 m. Beach ridges are resolvable as ~90 to 150-cm thick stratified packages of gravelly sand overlying a prominent lakeward-dipping reflector, interpreted as the pre-lake land surface. Many ridges contain a package of sediment resembling a buried berm at their core, typically offset in a landward direction from the geomorphic crest of the beach ridge. Sequences of lakeward-dipping reflectors are resolvable beneath the beach face of all ridges. No evidence was observed to indicate that beach ridges were submerged by higher water levels after their formation. Instead, the GPR data are consistent with a model of sequential ridge formation during a monotonic lake regression.
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de Boer, Paul M. C., and Christian M. Hafner. "Ridge regression revisited." Statistica Neerlandica 59, no. 4 (October 13, 2005): 498–505. http://dx.doi.org/10.1111/j.1467-9574.2005.00304.x.

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Hertz, D. "Sequential ridge regression." IEEE Transactions on Aerospace and Electronic Systems 27, no. 3 (May 1991): 571–74. http://dx.doi.org/10.1109/7.81440.

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Tutz, Gerhard, and Harald Binder. "Boosting ridge regression." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 6044–59. http://dx.doi.org/10.1016/j.csda.2006.11.041.

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Sundberg, Rolf. "Continuum Regression and Ridge Regression." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 3 (July 1993): 653–59. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01930.x.

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Dissertations / Theses on the topic "Regresión de Ridge"

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Williams, Ulyana P. "On Some Ridge Regression Estimators for Logistic Regression Models." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3667.

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The purpose of this research is to investigate the performance of some ridge regression estimators for the logistic regression model in the presence of moderate to high correlation among the explanatory variables. As a performance criterion, we use the mean square error (MSE), the mean absolute percentage error (MAPE), the magnitude of bias, and the percentage of times the ridge regression estimator produces a higher MSE than the maximum likelihood estimator. A Monto Carlo simulation study has been executed to compare the performance of the ridge regression estimators under different experimental conditions. The degree of correlation, sample size, number of independent variables, and log odds ratio has been varied in the design of experiment. Simulation results show that under certain conditions, the ridge regression estimators outperform the maximum likelihood estimator. Moreover, an empirical data analysis supports the main findings of this study. This thesis proposed and recommended some good ridge regression estimators of the logistic regression model for the practitioners in the field of health, physical and social sciences.
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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.
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Younker, James. "Ridge Estimation and its Modifications for Linear Regression with Deterministic or Stochastic Predictors." Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22662.

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A common problem in multiple regression analysis is having to engage in a bias variance trade-off in order to maximize the performance of a model. A number of methods have been developed to deal with this problem over the years with a variety of strengths and weaknesses. Of these approaches the ridge estimator is one of the most commonly used. This paper conducts an examination of the properties of the ridge estimator and several alternatives in both deterministic and stochastic environments. We find the ridge to be effective when the sample size is small relative to the number of predictors. However, we also identify a few cases where some of the alternative estimators can outperform the ridge estimator. Additionally, we provide examples of applications where these cases may be relevant.
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Kuhl, Mark R. "Ridge regression signal processing applied to multisensor position fixing." Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1183651058.

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Zaldivar, Cynthia. "On the Performance of some Poisson Ridge Regression Estimators." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3669.

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Multiple regression models play an important role in analyzing and making predictions about data. Prediction accuracy becomes lower when two or more explanatory variables in the model are highly correlated. One solution is to use ridge regression. The purpose of this thesis is to study the performance of available ridge regression estimators for Poisson regression models in the presence of moderately to highly correlated variables. As performance criteria, we use mean square error (MSE), mean absolute percentage error (MAPE), and percentage of times the maximum likelihood (ML) estimator produces a higher MSE than the ridge regression estimator. A Monte Carlo simulation study was conducted to compare performance of the estimators under three experimental conditions: correlation, sample size, and intercept. It is evident from simulation results that all ridge estimators performed better than the ML estimator. We proposed new estimators based on the results, which performed very well compared to the original estimators. Finally, the estimators are illustrated using data on recreational habits.
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Wissel, Julia. "A new biased estimator for multivariate regression models with highly collinear variables." Doctoral thesis, kostenfrei, 2009. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2009/3638/.

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Bakshi, Girish. "Comparison of ridge regression and neural networks in modeling multicollinear data." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178815205.

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Li, Ying. "A Comparison Study of Principle Component Regression, Partial Least Square Regression and Ridge Regression with Application to FTIR Data." Thesis, Uppsala University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-127983.

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Least squares estimator may fail when the number of explanatory vari-able is relatively large in comparison to the sample or if the variablesare almost collinear. In such a situation, principle component regres-sion, partial least squares regression and ridge regression are oftenproposed methods and widely used in many practical data analysis,especially in chemometrics. They provide biased coecient estima-tors with the relatively smaller variation than the variance of the leastsquares estimator. In this paper, a brief literature review of PCR,PLS and RR is made from a theoretical perspective. Moreover, a dataset is used, in order to examine their performance on prediction. Theconclusion is that for prediction PCR, PLS and RR provide similarresults. It requires substantial verication for any claims as to thesuperiority of any of the three biased regression methods.

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Silva, Tatiane Cazarin da. "Algoritmos primais-duais de ponto fixo aplicados ao problema Ridge Regression." reponame:Repositório Institucional da UFPR, 2016. http://hdl.handle.net/1884/43736.

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Orientador : Prof. Dr. Ademir Alves Ribeiro
Coorientador : Profª. Drª. Gislaine Aparecida Periçaro
Tese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 08/06/2016
Inclui referências : f. 60-64
Área de concentração : Progressão matemática
Resumo: Neste trabalho propomos algoritmos para resolver uma formulação primal-dual geral de ponto fixo aplicada ao problema de Ridge Regression. Estudamos a formulação primal para problemas de quadrados mínimos regularizado, em especial na norma L2, nomeados Ridge Regression e descrevemos a dualidade convexa para essa classe de problemas. Nossa estratégia foi considerar as formulações primal e dual conjuntamente, e minimizar o gap de dualidade entre elas. Estabelecemos o algoritmo de ponto fixo primal-dual, nomeado SRP e uma reformulação para esse método, contribuição principal da tese, a qual mostrou-se mais eficaz e robusta, designada por método acc-SRP, ou versão acelerada do método SRP. O estudo teórico dos algoritmos foi feito por meio da análise de propriedades espectrais das matrizes de iteração associadas. Provamos a convergência linear dos algoritmos e apresentamos alguns exemplos numéricos comparando duas variantes para cada algoritmo proposto. Mostramos também que o nosso melhor método, acc-SRP, possui excelente desempenho numérico na resolução de problemas muito mal-condicionados quando comparado ao Método de Gradientes Conjugados, o que o torna computacionalmente mais atraente. Palavras-chave: Métodos primais-duais, Ridge Regression, ponto fixo, dualidade, métodos acelerados
Abstract: In this work we propose algorithms for solving a fixed-point general primal-dual formulation applied to the Ridge Regression problem. We study the primal formulation for regularized least squares problems, especially L2-norm, named Ridge Regression and then describe convex duality for that class of problems. Our strategy was to consider together primal and dual formulations and minimize the duality gap between them. We established the primal-dual fixed point algorithm, named SRP and a reformulation for this method, the main contribution of the thesis, which was more efficient and robust, called acc-SRP method or accelerated version of the SRP method. The theoretical study of the algorithms was done through the analysis of the spectral properties of the associated iteration matrices. We proved the linear convergence of algorithms and some numerical examples comparing two variants for each algorithm proposed were presented. We also showed that our best method, acc-SRP, has excellent numerical performance for solving very ill-conditioned problems, when compared to the conjugate gradient method, which makes it computationally more attractive. Key-words: Primal-dual methods, ridge regression, fixed point, duality, accelerated methods.
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Saha, Angshuman. "Application of ridge regression for improved estimation of parameters in compartmental models /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8945.

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Books on the topic "Regresión de Ridge"

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Gruber, Marvin H. J. Regression estimators: A comparative study. 2nd ed. Baltimore: Johns Hopkins University Press, 2010.

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Saleh, A. K. Md Ehsanes, Mohammad Arashi, and B. M. Golam Kibria, eds. Theory of Ridge Regression Estimation with Applications. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781118644478.

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Coshall, John. An illustration of ridge regression using agricultural data. London: University of North London Press, 1993.

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Improving efficiency by shrinkage: The James-Stein and ridge regression estimators. New York: Marcel Dekker, 1998.

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Gruber, Marvin H. J. Regression estimators: A comparative study. Boston: Academic Press, 1990.

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Gruber, Marvin H. J. Regression estimators: A comparative study. Boston: Academic Press, 1992.

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Regression estimators: A comparative study. 2nd ed. Baltimore: Johns Hopkins University Press, 2010.

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Gruber, Marvin H. J. Regression estimators: A comparative study. 2nd ed. Baltimore: Johns Hopkins University Press, 2010.

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Verallgemeinerte Ridge Regression: Eine Untersuchung von theoretischen Eigenschaften und der Operationalität verzerrter Schätzer im linearen Modell. Frankfurt am Main: A. Hain, 1986.

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Ahmed, S. E. (Syed Ejaz), 1957- editor of compilation, ed. Perspectives on big data analysis: Methodologies and applications : International Workshop on Perspectives on High-Dimensional Data Anlaysis II, May 30-June 1, 2012, Centre de Recherches Mathématiques, University de Montréal, Montréal, Québec, Canada. Providence, Rhode Island: American Mathematical Society, 2014.

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Book chapters on the topic "Regresión de Ridge"

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Vovk, Vladimir. "Kernel Ridge Regression." In Empirical Inference, 105–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6_11.

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Vinod, H. D. "Confidence Intervals for Ridge Regression Parameters." In Time Series and Econometric Modelling, 279–300. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-4790-0_19.

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Papadopoulos, Harris. "Cross-Conformal Prediction with Ridge Regression." In Statistical Learning and Data Sciences, 260–70. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17091-6_21.

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Zhdanov, Fedor, and Yuri Kalnishkan. "An Identity for Kernel Ridge Regression." In Lecture Notes in Computer Science, 405–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16108-7_32.

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Cawley, Gavin C., Nicola L. C. Talbot, and Olivier Chapelle. "Estimating Predictive Variances with Kernel Ridge Regression." In Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, 56–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11736790_5.

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Wang, Ling, Liefeng Bo, and Licheng Jiao. "Sparse Kernel Ridge Regression Using Backward Deletion." In Lecture Notes in Computer Science, 365–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_40.

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Shigeto, Yutaro, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto. "Ridge Regression, Hubness, and Zero-Shot Learning." In Machine Learning and Knowledge Discovery in Databases, 135–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23528-8_9.

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Ohishi, Mineaki, Hirokazu Yanagihara, and Hirofumi Wakaki. "Optimization of Generalized $$C_p$$ Criterion for Selecting Ridge Parameters in Generalized Ridge Regression." In Intelligent Decision Technologies, 267–78. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5925-9_23.

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Shu, Xin, and Hongtao Lu. "Neighborhood Structure Preserving Ridge Regression for Dimensionality Reduction." In Communications in Computer and Information Science, 25–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33506-8_4.

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Zuliana, Sri Utami, and Aris Perperoglou. "The Weight of Penalty Optimization for Ridge Regression." In Analysis of Large and Complex Data, 231–39. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25226-1_20.

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Conference papers on the topic "Regresión de Ridge"

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Siripurapu, Sundeep Krishna, and Anthony F. Luscher. "Modeling Shear Performance of High-Speed Ridged Nail in Aluminum Joints." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68309.

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The automobile industry is currently moving towards increasing fuel economy by reducing vehicle weight. A high-speed ridged nailing technology is a new technology which can solve the challenges involved with multi-material joining in automobile industry. This study investigated the performance of the high speed ridged nail joints made with Aluminum 6061-T6 in pure shear loading. An LS-DYNA FEA model of high-speed ridged nail joint was developed and model results were validated against corresponding experimental results from pure shear loading tests till failure. It was shown that the proposed shear model agreed with experimental results. A set of sensitivity studies were carried out to identify the influential material model type, influence of petalling and effect of ridge-engagement on the joint strength in shear. The model was further used to simulate performance of high-speed ridged nail joints with different thickness combinations. The findings of these simulations indicate that high-speed ridged nail is a viable solution for material joining. Regression models based on bottom plate thickness were proposed.
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Burnaev, Evgeny, and Ivan Nazarov. "Conformalized Kernel Ridge Regression." In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016. http://dx.doi.org/10.1109/icmla.2016.0017.

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Kakula, Siva K., Anthony J. Pinar, Timothy C. Havens, and Derek T. Anderson. "Choquet Integral Ridge Regression." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177657.

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He, Jinrong, Lixin Ding, Lei Jiang, and Ling Ma. "Kernel ridge regression classification." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889396.

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Yang, Changmao, Jie Xu, and Sicong Gong. "Objective Ridge Regression System." In 2020 7th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2020. http://dx.doi.org/10.1109/icisce50968.2020.00192.

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An, Senjian, Wanquan Liu, and Svetha Venkatesh. "Face Recognition Using Kernel Ridge Regression." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383105.

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Gratton, Cristiano, Naveen K. D. Venkategowda, Reza Arablouei, and Stefan Werner. "Distributed Ridge Regression with Feature Partitioning." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645549.

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Tanaka, Akira. "Mathematical Interpretations of Kernel Ridge Regression." In COMPUTING ANTICIPATORY SYSTEMS: CASYS'05 - Seventh International Conference. AIP, 2006. http://dx.doi.org/10.1063/1.2216644.

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Dimitrov, S., S. Kovacheva, K. Prodanova, and Michail D. Todorov. "Realization of Ridge Regression in MATLAB." In APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS: Proceedings of the 34th Conference on Applications of Mathematics in Engineering and Economics (AMEE '08). AIP, 2008. http://dx.doi.org/10.1063/1.3030819.

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Chavez, Gustavo, Yang Liu, Pieter Ghysels, Xiaoye Sherry Li, and Elizaveta Rebrova. "Scalable and Memory-Efficient Kernel Ridge Regression." In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2020. http://dx.doi.org/10.1109/ipdps47924.2020.00102.

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