Literatura académica sobre el tema "Nonlinear regression analysi"
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Artículos de revistas sobre el tema "Nonlinear regression analysi"
Bukac, Josef. "Weighted nonlinear regression". Analysis in Theory and Applications 24, n.º 4 (diciembre de 2008): 330–35. http://dx.doi.org/10.1007/s10496-008-0330-y.
Texto completoVerboon, Peter. "Robust nonlinear regression analysis". British Journal of Mathematical and Statistical Psychology 46, n.º 1 (mayo de 1993): 77–94. http://dx.doi.org/10.1111/j.2044-8317.1993.tb01003.x.
Texto completoNg, Meei Pyng y Gary K. Grunwald. "Nonlinear Regression Analysis of the Joint-Regression Model". Biometrics 53, n.º 4 (diciembre de 1997): 1366. http://dx.doi.org/10.2307/2533503.
Texto completoKass, Robert E., Douglas M. Bates, Donald G. Watts, G. A. F. Seber y C. J. Wild. "Nonlinear Regression Analysis and Its Applications." Journal of the American Statistical Association 85, n.º 410 (junio de 1990): 594. http://dx.doi.org/10.2307/2289810.
Texto completoHowell, Roy D., Douglas M. Bates y Donald G. Watts. "Nonlinear Regression Analysis & Its Application". Journal of Marketing Research 27, n.º 1 (febrero de 1990): 113. http://dx.doi.org/10.2307/3172558.
Texto completoHung, Hsien-Ming. "Nonlinear regression analysis for complex surveys1". Communications in Statistics - Theory and Methods 19, n.º 9 (enero de 1990): 3447–70. http://dx.doi.org/10.1080/03610929008830390.
Texto completoSlepicka, James S. y Soyoung S. Cha. "Stabilized nonlinear regression for interferogram analysis". Applied Optics 34, n.º 23 (10 de agosto de 1995): 5039. http://dx.doi.org/10.1364/ao.34.005039.
Texto completoMilliken, George A. "Nonlinear Regression Analysis and Its Applications". Technometrics 32, n.º 2 (mayo de 1990): 219–20. http://dx.doi.org/10.1080/00401706.1990.10484638.
Texto completoEfremov, G. I., T. Yu Zhuravleva y B. S. Sazhin. "Data processing by nonlinear regression analysis". Theoretical Foundations of Chemical Engineering 34, n.º 2 (marzo de 2000): 194–96. http://dx.doi.org/10.1007/bf02757840.
Texto completoYe, Ya-Fen, Chao Ying, Yuan-Hai Shao, Chun-Na Li y Yu-Juan Chen. "Robust and SparseLP-Norm Support Vector Regression". Journal of Advanced Computational Intelligence and Intelligent Informatics 21, n.º 6 (20 de octubre de 2017): 989–97. http://dx.doi.org/10.20965/jaciii.2017.p0989.
Texto completoTesis sobre el tema "Nonlinear regression analysi"
Lopresti, Mattia. "Non-destructive X-ray based characterization of materials assisted by multivariate methods of data analysis: from theory to application". Doctoral thesis, Università del Piemonte Orientale, 2022. http://hdl.handle.net/11579/143020.
Texto completoNARBAEV, TIMUR. "Forecasting cost at completion with growth models and Earned Value Management". Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2506248.
Texto completoSulieman, Hana. "Parametric sensitivity analysis in nonlinear regression". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0004/NQ27858.pdf.
Texto completoCarvalho, Renato de Souza. "Nonlinear regression application to well test analysis /". Access abstract and link to full text, 1993. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/9416602.
Texto completoNeugebauer, Shawn Patrick. "Robust Analysis of M-Estimators of Nonlinear Models". Thesis, Virginia Tech, 1996. http://hdl.handle.net/10919/36557.
Texto completoMaster of Science
Galarza, Morales Christian Eduardo 1988. "Quantile regression for mixed-effects models = Regressão quantílica para modelos de efeitos mistos". [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306681.
Texto completoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
Made available in DSpace on 2018-08-27T06:40:31Z (GMT). No. of bitstreams: 1 GalarzaMorales_ChristianEduardo_M.pdf: 5076076 bytes, checksum: 0967f08c9ad75f9e7f5df339563ef75a (MD5) Previous issue date: 2015
Resumo: Os dados longitudinais são frequentemente analisados usando modelos de efeitos mistos normais. Além disso, os métodos de estimação tradicionais baseiam-se em regressão na média da distribuição considerada, o que leva a estimação de parâmetros não robusta quando a distribuição do erro não é normal. Em comparação com a abordagem de regressão na média convencional, a regressão quantílica (RQ) pode caracterizar toda a distribuição condicional da variável de resposta e é mais robusta na presença de outliers e especificações erradas da distribuição do erro. Esta tese desenvolve uma abordagem baseada em verossimilhança para analisar modelos de RQ para dados longitudinais contínuos correlacionados através da distribuição Laplace assimétrica (DLA). Explorando a conveniente representação hierárquica da DLA, a nossa abordagem clássica segue a aproximação estocástica do algoritmo EM (SAEM) para derivar estimativas de máxima verossimilhança (MV) exatas dos efeitos fixos e componentes de variância em modelos lineares e não lineares de efeitos mistos. Nós avaliamos o desempenho do algoritmo em amostras finitas e as propriedades assintóticas das estimativas de MV através de experimentos empíricos e aplicações para quatro conjuntos de dados reais. Os algoritmos SAEMs propostos são implementados nos pacotes do R qrLMM() e qrNLMM() respectivamente
Abstract: Longitudinal data are frequently analyzed using normal mixed effects models. Moreover, the traditional estimation methods are based on mean regression, which leads to non-robust parameter estimation for non-normal error distributions. Compared to the conventional mean regression approach, quantile regression (QR) can characterize the entire conditional distribution of the outcome variable and is more robust to the presence of outliers and misspecification of the error distribution. This thesis develops a likelihood-based approach to analyzing QR models for correlated continuous longitudinal data via the asymmetric Laplace distribution (ALD). Exploiting the nice hierarchical representation of the ALD, our classical approach follows the stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood (ML) estimates of the fixed-effects and variance components in linear and nonlinear mixed effects models. We evaluate the finite sample performance of the algorithm and the asymptotic properties of the ML estimates through empirical experiments and applications to four real life datasets. The proposed SAEMs algorithms are implemented in the R packages qrLMM() and qrNLMM() respectively
Mestrado
Estatistica
Mestre em Estatística
Cui, Chenhao. "Nonlinear multiple regression methods for spectroscopic analysis : application to NIR calibration". Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10058694/.
Texto completoFernández-Val, Iván. "Three essays on nonlinear panel data models and quantile regression analysis". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32408.
Texto completoIncludes bibliographical references.
This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force participation, union membership, migration, purchase of durable goods, marital status, or fertility. The second chapter looks at generalized method of moments estimation in panel data models with individual-specific parameters. An important example of these models is a random coefficients linear model with endogenous regressors. The third chapter (co-authored with Joshua Angrist and Victor Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.
by Iván Fernández-Val.
Ph.D.
Hyung, Namwon. "Essays on panel and nonlinear time series analysis /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9958858.
Texto completoArai, Yoichi. "Nonlinear nonstationary time series analysis and its application /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3144311.
Texto completoLibros sobre el tema "Nonlinear regression analysi"
1952-, Wild C. J., ed. Nonlinear regression. New York: Wiley, 1989.
Buscar texto completoSeber, G. A. F. Nonlinear regression. Hoboken, N.J: Wiley-Interscience, 2003.
Buscar texto completoIvanov, A. V. Asymptotic theory of nonlinear regression. Dordrecht: Kluwer Academic Publishers, 1997.
Buscar texto completoBates, Douglas M. y Donald G. Watts, eds. Nonlinear Regression Analysis and Its Applications. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1988. http://dx.doi.org/10.1002/9780470316757.
Texto completoG, Watts Donald, ed. Nonlinear regression analysis and its applications. New York: Wiley, 1988.
Buscar texto completoBorowiak, Dale S. Model discrimination for nonlinear regression models. New York: M. Dekker, 1989.
Buscar texto completoHandbook of nonlinear regression models. New York: M. Dekker, 1990.
Buscar texto completoPázman, Andrej. Nonlinear statistical models. Dordrecht: Kluwer Academic Publishers, 1993.
Buscar texto completoAsymptotic Theory of Nonlinear Regression. Dordrecht: Springer Netherlands, 1997.
Buscar texto completoNonlinear statistical models. New York: Wiley, 1987.
Buscar texto completoCapítulos de libros sobre el tema "Nonlinear regression analysi"
Cleophas, Ton J. y Aeilko H. Zwinderman. "More on Nonlinear Regressions". En Regression Analysis in Medical Research, 279–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71937-5_18.
Texto completoCleophas, Ton J. y Aeilko H. Zwinderman. "More on Nonlinear Regressions". En Regression Analysis in Medical Research, 291–312. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61394-5_18.
Texto completoJudd, Charles M., Gary H. McClelland y Carey S. Ryan. "Moderated and Nonlinear Regression Models". En Data Analysis, 135–67. Third Edition. | New York : Routledge, 2017. | Revised edition: Routledge, 2017. http://dx.doi.org/10.4324/9781315744131-7.
Texto completoArmstrong, Richard A. y Anthony C. Hilton. "Nonlinear Regression: Fitting an Exponential Curve". En Statistical Analysis in Microbiology: Statnotes, 109–12. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch21.
Texto completoArmstrong, Richard A. y Anthony C. Hilton. "Nonlinear Regression: Fitting A Logistic Growth Curve". En Statistical Analysis in Microbiology: Statnotes, 119–22. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch23.
Texto completoKnopov, Pavel S. y Arnold S. Korkhin. "Asymptotic Properties of Parameters in Nonlinear Regression Models". En Regression Analysis Under A Priori Parameter Restrictions, 29–71. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0574-0_2.
Texto completoFraser, Cynthia. "Sensitivity Analysis with Nonlinear Multiple Regression Models". En Business Statistics for Competitive Advantage with Excel 2013, 433–46. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7381-7_14.
Texto completoBagchi, Jayri y Tapas Si. "Nonlinear Regression Analysis Using Multi-verse Optimizer". En Algorithms for Intelligent Systems, 45–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_4.
Texto completoArmstrong, Richard A. y Anthony C. Hilton. "Nonlinear Regression: Fitting A General Polynomial-Type Curve". En Statistical Analysis in Microbiology: Statnotes, 113–18. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch22.
Texto completode Vries, Harm, George Azzopardi, André Koelewijn y Arno Knobbe. "Parametric Nonlinear Regression Models for Dike Monitoring Systems". En Advances in Intelligent Data Analysis XIII, 345–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12571-8_30.
Texto completoActas de conferencias sobre el tema "Nonlinear regression analysi"
Yu, Enxi y Soyoung S. Cha. "Two-dimensional nonlinear regression for interferogram analysis". En SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, editado por Soyoung S. Cha y James D. Trolinger. SPIE, 1995. http://dx.doi.org/10.1117/12.221534.
Texto completoKim, Sunjoong, Billie F. (Jr) Spencer, Ho-Kyung Kim, Se-Jin Kim y Doyun Hwang. "Data-driven modeling of modal parameters of long-span bridges under environmental and operational variation". En IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2020. http://dx.doi.org/10.2749/seoul.2020.170.
Texto completoYin, Zhiyao, Patrick Nau y Hannah Scheffold. "CNN-based tomographic reconstruction of laser absorption in a gas turbine model combustor". En Laser Applications to Chemical, Security and Environmental Analysis. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/lacsea.2022.lf1c.5.
Texto completoUkwu, Austin K., Mike O. Onyekonwu y Sunday S. Ikiensikimama. "Decline Curve Analysis using Combined Linear and Nonlinear Regression". En SPE Nigeria Annual International Conference and Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/178295-ms.
Texto completoIvanov, A., D. Voynikova, S. Gocheva-Ilieva, H. Kulina y I. Iliev. "Using principal component analysis and general path seeker regression for investigation of air pollution and CO modeling". En RECENT DEVELOPMENTS IN NONLINEAR ACOUSTICS: 20th International Symposium on Nonlinear Acoustics including the 2nd International Sonic Boom Forum. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4934341.
Texto completoPerichiappan Perichappan, Kumar Attangudi, Sriramakrishnan Chandrasekaran y Hayk Sargsyan. "Comparative Analysis of Astrophysical Data by Different Nonlinear Regression Strategies". En 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2018. http://dx.doi.org/10.1109/macs.2018.8628339.
Texto completoNassif, Ali Bou, Manar AbuTalib y Luiz Fernando Capretz. "Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis". En 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2020. http://dx.doi.org/10.1109/ccece47787.2020.9255712.
Texto completoOnur, Mustafa y Fikri J. Kuchuk. "Nonlinear Regression Analysis of Well-Test Pressure Data with Uncertain Variance". En SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2000. http://dx.doi.org/10.2118/62918-ms.
Texto completoBalouch, Ammar Suhail. "Reducing sensors using nonlinear regressions analysis of stored measurements (ReSUNoRA)". En 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 2016. http://dx.doi.org/10.1109/icbdsc.2016.7460354.
Texto completoSchmidt, Michael D. y Hod Lipson. "Data-Mining Dynamical Systems: Automated Symbolic System Identification for Exploratory Analysis". En ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59309.
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