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Everything about Nonlinear regression
Regression analysis
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations (iterations). Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Nonlinear regressionA 5-minute orientation
- Skim a recent paper: Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban AreaInfluential work since 2022
- Pick up a classic book: Nonlinear models for repeated measurement dataThe most-reprinted book on the subject
- Read the landmark paper: Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear EstimationCited 6,754 times
- Go sideways: Total least squaresA closely related topic
- Go sideways: Levenberg–Marquardt algorithmA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
Nonlinear Regression with Dependent Observations
Nonlinear Regression
Nonlinear regression using spreadsheets
Nonlinear Regression with Autocorrelated Errors
Self Modeling Nonlinear Regression
Heteroscedastic Nonlinear Regression
Covariate-adjusted nonlinear regression
🆕 Influential since 2022
Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban Area
Spiking neural networks for nonlinear regression
Nonlinear Regression Modelling: A Primer with Applications and Caveats
Densely Connected Neural Networks for Nonlinear Regression
Fuzzy Nonlinear Regression Modeling With Radial Basis Function Networks
đź“š Books worth reading
Nonlinear models for repeated measurement dataMarie Davidian, David .M. Giltinan · 1995
Measurement error in nonlinear modelsRaymond J. Carroll, David Ruppert · 1995
Optimal Design For Nonlinear Response ModelsValerii V. Fedorov · 2012
Robust Nonlinear RegressionHossein Riazoshams, Habshah Midi · 2018
Statistical tools for nonlinear regressionSylvie Huet, Annie Bouvier · 1996
Nonlinear Statistical ModelsAndrej Pázman · 1899
Statistical Methods of Model BuildingHelga Bunke, Olaf Bunke · 1989
Nonlinear regressionG. A. F. Seber · 1989
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
- Total least squares In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational…
- Levenberg–Marquardt algorithm In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve…
- Multinomial logistic regression In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than…
- Bayesian linear regression Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables…
- Gauss–Markov theorem In statistics, the Gauss–Markov theorem (or simply Gauss theorem for some authors) states that, under certain conditions, the ordinary least squares (OLS)…
- Vector generalized linear model In statistics, the class of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models…
- Heteroskedasticity-consistent standard errors The topic of heteroskedasticity-consistent (HC) standard errors arises in statistics and econometrics in the context of linear regression and time series…
- Principal component regression In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form…