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A general framework for robust compressive sensing based nonlinear regression
2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012 In this paper, we present a general framework for robust nonlinear regression that leverages concepts from the field of compressive sensing to simultaneously detect outliers and determine optimally sparse representations of noisy data from arbitrary sets of basis functions.
Brian E. Moore +1 more
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Journal of Regional Science, 2001 There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques.
Thomas de Graaff +3 more
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Nonlinear Regression of Ink Spreading Using a General Ellipse Model
2022 22nd International Conference on Control, Automation and Systems (ICCAS), 2022 Hyun Soo Kim, Junghwan Kim, Miyeon Kwon
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Asymptotic Confidence Bands for Generalized Nonlinear Regression Models
Biometrics, 1995Asymptotic confidence bands for generalized nonlinear regression models are developed. These are based on a combination of the S method of Scheffe, together with the delta method which is used to approximate the mean function by a linear combination of the parameters. The approach can be used in any situation where large sample theory can be applied to
Cox, Christopher, Ma, Guangqin
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Nonlinear regression technique applied to generalized phase-shifting interferometry
Journal of Modern Optics, 2005We develop a new approach involving nonlinear regression in phase-shifting interferometry with the hope of improving the accuracy of phase measurement in the presence of first-order piezoelectric transducer (PZT) calibration errors. The approach that uses the Levenberg–Marquardt method is shown to detect with high precision the value of the true phase ...
Abhijit Patil, Pramod Rastogi
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Nonlinear process monitoring based on decentralized generalized regression neural networks
Expert Systems with Applications, 2020Abstract Given that the main task of process monitoring (i.e., fault detection) is actually a classical one-class classification problem, the generalized regression neural network (GRNN) is directly inapplicable for handling process modeling and monitoring issues.
Ting Lan +4 more
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Structural identifiability of generalized constraint neural network models for nonlinear regression
Neurocomputing, 2008Identifiability becomes an essential requirement for learning machines when the models contain physically interpretable parameters. This paper presents two approaches to examining structural identifiability of the generalized constraint neural network (GCNN) models by viewing the model from two different perspectives.
Yang, Shuang-Hong +2 more
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Generalized Linear and Nonlinear Regression
2014Multiple linear regression is a powerful method of exploring relationships between a response Y and a set of potential explanatory variables \(x_1,\ldots , x_p\), but it has an obvious limitation: it assumes the predictive relationship is, on average, linear.
Robert E. Kass +2 more
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On generalized elliptical quantiles in the nonlinear quantile regression setup
TEST, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hlubinka, Daniel, Šiman, Miroslav
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Confirmation of multiple outliers in generalized linear and nonlinear regressions
Computational Statistics & Data Analysis, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fung, WK, Lee, AH
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