Relevant bibliographies by topics / Nonlinear regression analysi

Academic literature on the topic 'Nonlinear regression analysi'

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Published: 14 March 2023

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Journal articles on the topic "Nonlinear regression analysi"

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Bukac, Josef. "Weighted nonlinear regression." Analysis in Theory and Applications 24, no. 4 (December 2008): 330–35. http://dx.doi.org/10.1007/s10496-008-0330-y.

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Verboon, Peter. "Robust nonlinear regression analysis." British Journal of Mathematical and Statistical Psychology 46, no. 1 (May 1993): 77–94. http://dx.doi.org/10.1111/j.2044-8317.1993.tb01003.x.

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Ng, Meei Pyng, and Gary K. Grunwald. "Nonlinear Regression Analysis of the Joint-Regression Model." Biometrics 53, no. 4 (December 1997): 1366. http://dx.doi.org/10.2307/2533503.

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Kass, Robert E., Douglas M. Bates, Donald G. Watts, G. A. F. Seber, and C. J. Wild. "Nonlinear Regression Analysis and Its Applications." Journal of the American Statistical Association 85, no. 410 (June 1990): 594. http://dx.doi.org/10.2307/2289810.

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Howell, Roy D., Douglas M. Bates, and Donald G. Watts. "Nonlinear Regression Analysis & Its Application." Journal of Marketing Research 27, no. 1 (February 1990): 113. http://dx.doi.org/10.2307/3172558.

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Hung, Hsien-Ming. "Nonlinear regression analysis for complex surveys1." Communications in Statistics - Theory and Methods 19, no. 9 (January 1990): 3447–70. http://dx.doi.org/10.1080/03610929008830390.

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Slepicka, James S., and Soyoung S. Cha. "Stabilized nonlinear regression for interferogram analysis." Applied Optics 34, no. 23 (August 10, 1995): 5039. http://dx.doi.org/10.1364/ao.34.005039.

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Milliken, George A. "Nonlinear Regression Analysis and Its Applications." Technometrics 32, no. 2 (May 1990): 219–20. http://dx.doi.org/10.1080/00401706.1990.10484638.

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Efremov, G. I., T. Yu Zhuravleva, and B. S. Sazhin. "Data processing by nonlinear regression analysis." Theoretical Foundations of Chemical Engineering 34, no. 2 (March 2000): 194–96. http://dx.doi.org/10.1007/bf02757840.

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Ye, Ya-Fen, Chao Ying, Yuan-Hai Shao, Chun-Na Li, and Yu-Juan Chen. "Robust and SparseLP-Norm Support Vector Regression." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 6 (October 20, 2017): 989–97. http://dx.doi.org/10.20965/jaciii.2017.p0989.

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A robust and sparseLp-norm support vector regression (Lp-RSVR) is proposed in this paper. The implementation of feature selection in ourLp-RSVR not only preserves the performance of regression but also improves its robustness. The main characteristics ofLp-RSVR are as follows: (i) By using the absolute constraint,Lp-RSVR performs robustly against outliers. (ii)Lp-RSVR ensures that useful features are selected based on theoretical analysis. (iii) Based on the feature-selection results, nonlinearLp-RSVR can be used when data is structurally nonlinear. Experimental results demonstrate the superiorities of the proposedLp-RSVR in both feature selection and regression performance as well as its robustness.
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Dissertations / Theses on the topic "Nonlinear regression analysi"

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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.

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X-ray based non-destructive techniques are an increasingly important tool in many fields, ranging from industry to fine arts, from medicine to basic research. Over the last century, the study of the physical phenomena underlying the interaction between X-rays and matter has led to the development of many different techniques suitable for morphological, textural, elementary, and compositional analysis. Furthermore, with the development of the hardware technology and its automation thanks to IT advancements, enormous progress has been made also from the point of view of data collection and nowadays it is possible to carry out measurement campaigns by collecting many GigaBytes of data in a few hours. Already huge data sets are further enlarged when samples are analyzed with a multi-technique approach and/or at in situ conditions with time, space, temperature, and concentration becoming additional variables. In the present work, new data collection and analysis methods are presented along with applicative studies in which innovative materials have been developed and characterized. These materials are currently of high application interest and involve composites for radiation protection, ultralight magnesium alloys and eutectic mixtures. The new approaches have been grown up from an instrumental viewpoint and with regard to the analysis of the data obtained, for which the use and development of multivariate methods was central. In this context, extensive use has been made of principal component analysis and experimental design methods. One prominent topic of the study involved the development of in situ analysis methods of evolving samples as a response to different types of gradients. In fact, while in large structures such as synchrotrons carrying out analyzes under variable conditions is now consolidated practice, on a laboratory scale this type of experiments is still relatively young and the methods of data analysis of data sets evolving systems have large perspectives for development especially, if integrated by multivariate methods.
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NARBAEV, TIMUR. "Forecasting cost at completion with growth models and Earned Value Management." Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2506248.

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Reliable forecasting of the final cost at completion is one of the vital components of project monitoring. Accuracy and stability in the forecast of an ongoing project is a critical criterion that ensures the project’s on budget and timely completion. The purpose of this dissertation is to develop a new Cost Estimate at Completion (CEAC) methodology to assist project managers in the task of forecasting the final cost at completion of ongoing projects. This forecasting methodology interpolates intrinsic characteristics of an S-shaped growth model and combines the Earned Schedule (ES) concepts into its equation to provide more accurate and stable cost estimates. Widely used conventional index-based methods for CEAC have inherent limitations such as reliance on past performance only, unreliable forecasts in early stages of a project life, and no count of forecasting statistics. To achieve its purpose the dissertation carried out five tasks. It, first, developed the method’s equation based on the integration of the four candidate S-shaped models and the earned schedule concepts. Second, the models’ equations were tested on past projects to assess their applicability and, then, the accuracy of CEACs was compared with ones found by the Cost Performance Index (CPI)-based formula. The scope of third task included comparing CEACs found by statistically valid and the most accurate Gompertz model (GM)-based equation against ones computed with the CPI-based method at each time point of the projects life. Then, the stability test was performed to determine if the method, with its corresponding performance indices that achieves the earlier stability, provides more accurate CEAC. Finally, the analysis was conducted to determine the existence of a correlation between schedule progress and the CEAC accuracy. Based on the research results it was determined that the GM-based method is the only valid model for cost estimates in all three stages and it provides more accurate estimates than the CPI-based formula does. Further comparative analysis showed that the two (the GM and CPI-based) methods’ performance index that achieved the earlier stability provided more accurate CEACs for that method, and finally, the new methodology takes into account the schedule impact as a factor of the cost performance in forecasting the CEAC. The developed methodology enhances forecasting capabilities of the existing Earned Value Management methods by refining traditional index-based approach through nonlinear regression analysis. The main novelty of the research is that this is a cost-schedule integrated approach which interpolates characteristics of a sigmoidal growth model with the ES technique to calculate a project’s CEAC. Two major contributions are brought to the Project Management. First, the dissertation extends the body of knowledge by introducing the methodology which combined two separate methods in one statistical technique that, so far, have been considered as two separate streams of project management research. Second, this technique advances the project management practice as it is a practical cost-schedule integrated approach that takes into account schedule progress (advance/delay) as a factor of cost behavior in calculation of CEAC.
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Sulieman, 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.

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Carvalho, 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.

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Neugebauer, Shawn Patrick. "Robust Analysis of M-Estimators of Nonlinear Models." Thesis, Virginia Tech, 1996. http://hdl.handle.net/10919/36557.

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Estimation of nonlinear models finds applications in every field of engineering and the sciences. Much work has been done to build solid statistical theories for its use and interpretation. However, there has been little analysis of the tolerance of nonlinear model estimators to deviations from assumptions and normality. We focus on analyzing the robustness properties of M-estimators of nonlinear models by studying the effects of deviations from assumptions and normality on these estimators. We discuss St. Laurent and Cook's Jacobian Leverage and identify the relationship of the technique to the robustness concept of influence. We derive influence functions for M-estimators of nonlinear models and show that influence of position becomes, more generally, influence of model. The result shows that, for M-estimators, we must bound not only influence of residual but also influence of model. Several examples highlight the unique problems of nonlinear model estimation and demonstrate the utility of the influence function.
Master of Science
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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.

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Orientador: Víctor Hugo Lachos Dávila
Dissertação (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
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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/.

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Chemometrics has been applied to analyse near-infrared (NIR) spectra for decades. Linear regression methods such as partial least squares (PLS) regression and principal component regression (PCR) are simple and widely used solutions for spectroscopic calibration. My dissertation connects spectroscopic calibration with nonlinear machine learning techniques. It explores the feasibility of applying nonlinear methods for NIR calibration. Investigated nonlinear regression methods include least squares support vec- tor machine (LS-SVM), Gaussian process regression (GPR), Bayesian hierarchical mixture of linear regressions (HMLR) and convolutional neural networks (CNN). Our study focuses on the discussion of various design choices, interpretation of nonlinear models and providing novel recommendations and insights for the con- struction nonlinear regression models for NIR data. Performances of investigated nonlinear methods were benchmarked against traditional methods on multiple real-world NIR datasets. The datasets have differ- ent sizes (varying from 400 samples to 7000 samples) and are from various sources. Hypothesis tests on separate, independent test sets indicated that nonlinear methods give significant improvements in most practical NIR calibrations.
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Fernández-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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2005.
Includes 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.
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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.

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Arai, 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.

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Books on the topic "Nonlinear regression analysi"

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1952-, Wild C. J., ed. Nonlinear regression. New York: Wiley, 1989.

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Seber, G. A. F. Nonlinear regression. Hoboken, N.J: Wiley-Interscience, 2003.

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Ivanov, A. V. Asymptotic theory of nonlinear regression. Dordrecht: Kluwer Academic Publishers, 1997.

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Bates, Douglas M., and 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.

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G, Watts Donald, ed. Nonlinear regression analysis and its applications. New York: Wiley, 1988.

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Borowiak, Dale S. Model discrimination for nonlinear regression models. New York: M. Dekker, 1989.

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Handbook of nonlinear regression models. New York: M. Dekker, 1990.

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Pázman, Andrej. Nonlinear statistical models. Dordrecht: Kluwer Academic Publishers, 1993.

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Asymptotic Theory of Nonlinear Regression. Dordrecht: Springer Netherlands, 1997.

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Nonlinear statistical models. New York: Wiley, 1987.

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Book chapters on the topic "Nonlinear regression analysi"

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Cleophas, Ton J., and Aeilko H. Zwinderman. "More on Nonlinear Regressions." In Regression Analysis in Medical Research, 279–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71937-5_18.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "More on Nonlinear Regressions." In Regression Analysis in Medical Research, 291–312. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61394-5_18.

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Judd, Charles M., Gary H. McClelland, and Carey S. Ryan. "Moderated and Nonlinear Regression Models." In Data Analysis, 135–67. Third Edition. | New York : Routledge, 2017. | Revised edition: Routledge, 2017. http://dx.doi.org/10.4324/9781315744131-7.

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Armstrong, Richard A., and Anthony C. Hilton. "Nonlinear Regression: Fitting an Exponential Curve." In Statistical Analysis in Microbiology: Statnotes, 109–12. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch21.

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Armstrong, Richard A., and Anthony C. Hilton. "Nonlinear Regression: Fitting A Logistic Growth Curve." In Statistical Analysis in Microbiology: Statnotes, 119–22. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch23.

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Knopov, Pavel S., and Arnold S. Korkhin. "Asymptotic Properties of Parameters in Nonlinear Regression Models." In 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.

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Fraser, Cynthia. "Sensitivity Analysis with Nonlinear Multiple Regression Models." In 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.

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Bagchi, Jayri, and Tapas Si. "Nonlinear Regression Analysis Using Multi-verse Optimizer." In Algorithms for Intelligent Systems, 45–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_4.

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Armstrong, Richard A., and Anthony C. Hilton. "Nonlinear Regression: Fitting A General Polynomial-Type Curve." In Statistical Analysis in Microbiology: Statnotes, 113–18. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch22.

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de Vries, Harm, George Azzopardi, André Koelewijn, and Arno Knobbe. "Parametric Nonlinear Regression Models for Dike Monitoring Systems." In 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.

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Conference papers on the topic "Nonlinear regression analysi"

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Yu, Enxi, and Soyoung S. Cha. "Two-dimensional nonlinear regression for interferogram analysis." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Soyoung S. Cha and James D. Trolinger. SPIE, 1995. http://dx.doi.org/10.1117/12.221534.

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Kim, Sunjoong, Billie F. (Jr) Spencer, Ho-Kyung Kim, Se-Jin Kim, and Doyun Hwang. "Data-driven modeling of modal parameters of long-span bridges under environmental and operational variation." In 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.

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<p>This study develops the multivariate model of modal parameters under the high variability of structural responses and environmental conditions. The automated operational modal analysis procedure is implemented by synthesizing the algorithms of output-only system identification and density-based clustering algorithm. The Gaussian Process Regression is applied to accumulated modal estimates as well as corresponding environmental/operational conditions for examining the high degree of nonlinear variation in these monitoring data. The performance of the developed model is demonstrated for one-to-one regressions for multivariate structural health monitoring outputs in the presence of environmental and operational variation.</p>
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Yin, Zhiyao, Patrick Nau, and Hannah Scheffold. "CNN-based tomographic reconstruction of laser absorption in a gas turbine model combustor." In 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.

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Tomographic reconstruction of laser absorption of H2O is demonstrated in a swirl-stabilized gas turbine model combustor. Superior reconstruction performance is achieved against conventional methods using a nonlinear regression technique based primarily on convolutional neural networks.
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Ukwu, Austin K., Mike O. Onyekonwu, and Sunday S. Ikiensikimama. "Decline Curve Analysis using Combined Linear and Nonlinear Regression." In SPE Nigeria Annual International Conference and Exhibition. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/178295-ms.

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Ivanov, A., D. Voynikova, S. Gocheva-Ilieva, H. Kulina, and I. Iliev. "Using principal component analysis and general path seeker regression for investigation of air pollution and CO modeling." In 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.

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Perichiappan Perichappan, Kumar Attangudi, Sriramakrishnan Chandrasekaran, and Hayk Sargsyan. "Comparative Analysis of Astrophysical Data by Different Nonlinear Regression Strategies." In 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2018. http://dx.doi.org/10.1109/macs.2018.8628339.

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Nassif, Ali Bou, Manar AbuTalib, and Luiz Fernando Capretz. "Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis." In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2020. http://dx.doi.org/10.1109/ccece47787.2020.9255712.

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Onur, Mustafa, and Fikri J. Kuchuk. "Nonlinear Regression Analysis of Well-Test Pressure Data with Uncertain Variance." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2000. http://dx.doi.org/10.2118/62918-ms.

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Balouch, Ammar Suhail. "Reducing sensors using nonlinear regressions analysis of stored measurements (ReSUNoRA)." In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 2016. http://dx.doi.org/10.1109/icbdsc.2016.7460354.

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Schmidt, Michael D., and Hod Lipson. "Data-Mining Dynamical Systems: Automated Symbolic System Identification for Exploratory Analysis." In 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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This paper describes a new algorithm for automatically reverse-engineering symbolic analytical models of dynamical systems directly from experimental observations, for the purpose of modeling, control and exploratory analysis. The new algorithm builds on genetic programming techniques used in symbolic regression to infer differential equations from time series data. We introduce the core algorithm for building coherent mathematical models efficiently and then describe its application to system identification. The method is demonstrated on a number of nonlinear mechanical and biological systems.
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