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Heteroscedastic Nonlinear Regression
Technometrics, 1988Several parameter estimation methods for dealing with heteroscedasticity in nonlinear regression are described. These include variations on ordinary, weighted, iteratively reweighted, extended. and generalized least squares. Some of these variations are new, and one of them in particular, modified extended iteratively reweighted least squares (MEIRLS),
S. L. Beal, L. B. Sheiner
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Nonstationary nonlinear quantile regression
Econometric Reviews, 2016ABSTRACTThis study examines estimation and inference based on quantile regression for parametric nonlinear models with an integrated time series covariate. We first derive the limiting distribution of the nonlinear quantile regression estimator and then consider testing for parameter restrictions, when the regression function is specified as an ...
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SYMMETRIC NONLINEAR REGRESSION
ETS Research Report Series, 2007ABSTRACTAn estimation tool for symmetric univariate nonlinear regression is presented. The method is based on introducing a nontrivial set of affine coordinates for diffeomorphisms of the real line. The main ingredient making the computations possible is the Connes‐Moscovici Hopf algebra of these affine coordinates.
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2004
Abstract Nonlinear regression fits a model to your data. In most cases, your goal is to get back the best-fit values of the parameters in that model. If so, it is crucial that you pick a sensible model. If the model makes no sense, even if it fits the data well, you won’t be able to interpret the best-fit values.
Harvey Motulsky, Arthur Christopoulos
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Abstract Nonlinear regression fits a model to your data. In most cases, your goal is to get back the best-fit values of the parameters in that model. If so, it is crucial that you pick a sensible model. If the model makes no sense, even if it fits the data well, you won’t be able to interpret the best-fit values.
Harvey Motulsky, Arthur Christopoulos
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Multivariate Nonlinear Regression
2007This chapter continues the previous chapter but now we can have two or more independent (predictor, explanatory) variables, the \(\overline{X}_i\). Another change from Chapter 12 is now we will work with fuzzy trapezoidal (shaped) fuzzy numbers instead of fuzzy triangular (shaped) fuzzy numbers.
James J. Buckley, Leonard J. Jowers
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2019
In this chapter, nonlinear transformations are introduced that expand linear regression options to include situations in which marginal responses are either increasing or decreasing, rather than constant. We will explore Tukey’s Ladder of Powers to identify particular ways to efficiently rescale variables to produce valid models with superior fit.
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In this chapter, nonlinear transformations are introduced that expand linear regression options to include situations in which marginal responses are either increasing or decreasing, rather than constant. We will explore Tukey’s Ladder of Powers to identify particular ways to efficiently rescale variables to produce valid models with superior fit.
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2013
Chapter 6 discussed regression models that were intrinsically linear. In this chapter we present regression models that are inherently nonlinear in nature. When using these models, the exact form of the nonlinearity does not need to be known explicitly or specified prior to model training.
Max Kuhn, Kjell Johnson
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Chapter 6 discussed regression models that were intrinsically linear. In this chapter we present regression models that are inherently nonlinear in nature. When using these models, the exact form of the nonlinearity does not need to be known explicitly or specified prior to model training.
Max Kuhn, Kjell Johnson
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2015
Nonlinear models arise when E[y] is a nonlinear function of unknown parameters. Hypotheses about these parameters may be linear or nonlinear. Such models tend to be used when they are suggested by theoretical considerations or used to build non-linear behavior into a model.
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Nonlinear models arise when E[y] is a nonlinear function of unknown parameters. Hypotheses about these parameters may be linear or nonlinear. Such models tend to be used when they are suggested by theoretical considerations or used to build non-linear behavior into a model.
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