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(Non) linear regression modeling [PDF]
, 2004 We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or predict the dependent variables by means of the model. Such
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2011
A model is nonlinear if any of the partial derivatives with respect to any of the model parameters are dependent on any other model parameter or if any of the derivatives do not exist or are discontinuous. This chapter expands on the previous chapter and introduces nonlinear regression within a least squares (NLS) and maximum likelihood framework.
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A model is nonlinear if any of the partial derivatives with respect to any of the model parameters are dependent on any other model parameter or if any of the derivatives do not exist or are discontinuous. This chapter expands on the previous chapter and introduces nonlinear regression within a least squares (NLS) and maximum likelihood framework.
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1993
Although this book deals with nonlinear models, a short chapter on linear regression models may be useful, since by comparison with the linear case one can better understand some features of nonlinear models. Moreover, linear regression models are probably the most popular models in applications.
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Although this book deals with nonlinear models, a short chapter on linear regression models may be useful, since by comparison with the linear case one can better understand some features of nonlinear models. Moreover, linear regression models are probably the most popular models in applications.
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Multiple linear regression based model for the indoor temperature of mobile containers
Heliyon, 2022Zoltan Patonai +2 more
exaly
Non-linear regression model for wind turbine power curve
, 2017Mantas Marčiukaitis +5 more
semanticscholar +1 more source
A new uncertain linear regression model based on equation deformation
Soft Computing, 2021Shuai Wang, Yufu Ning, Hongmei Shi
exaly
2003
In this chapter, we consider point estimation of the parameters s ∈ ℝ P and σ2 ∈ (0, ∞) in the linear regression model $$y = X\beta + \varepsilon , \varepsilon \sim (0,{{\sigma }^{2}}{{I}_{n}}) $$ We will focus our attention to the ordinary least squares estimator $$ \hat \beta = (X'X)^{ - 1} X'y $$ and the least squares variance estimator
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In this chapter, we consider point estimation of the parameters s ∈ ℝ P and σ2 ∈ (0, ∞) in the linear regression model $$y = X\beta + \varepsilon , \varepsilon \sim (0,{{\sigma }^{2}}{{I}_{n}}) $$ We will focus our attention to the ordinary least squares estimator $$ \hat \beta = (X'X)^{ - 1} X'y $$ and the least squares variance estimator
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