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  2. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression. [1]

  3. Regularized least squares - Wikipedia

    en.wikipedia.org/wiki/Regularized_least_squares

    Ridge regression provides better accuracy in the case > for highly correlated variables. [2] In another case, n < d {\displaystyle n<d} , LASSO selects at most n {\displaystyle n} variables. Moreover, LASSO tends to select some arbitrary variables from group of highly correlated samples, so there is no grouping effect.

  4. Total least squares - Wikipedia

    en.wikipedia.org/wiki/Total_least_squares

    It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation of the data is generically equivalent to the best, in the Frobenius norm, low-rank approximation of the data matrix. [1]

  5. Outline of regression analysis - Wikipedia

    en.wikipedia.org/wiki/Outline_of_regression_analysis

    The following outline is provided as an overview of and topical guide to regression analysis: Regression analysis – use of statistical techniques for learning about the relationship between one or more dependent variables ( Y ) and one or more independent variables ( X ).

  6. Non-linear least squares - Wikipedia

    en.wikipedia.org/wiki/Non-linear_least_squares

    Consider a set of data points, (,), (,), …, (,), and a curve (model function) ^ = (,), that in addition to the variable also depends on parameters, = (,, …,), with . It is desired to find the vector of parameters such that the curve fits best the given data in the least squares sense, that is, the sum of squares = = is minimized, where the residuals (in-sample prediction errors) r i are ...

  7. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Optimal instruments regression is an extension of classical IV regression to the situation where E[ε i | z i] = 0. Total least squares (TLS) [6] is an approach to least squares estimation of the linear regression model that treats the covariates and response variable in a more geometrically symmetric manner than OLS. It is one approach to ...

  8. Ford to cut European jobs as EV shift, Chinese rivals take toll

    www.aol.com/news/ford-cut-14-european-jobs...

    Shares in Ford were down 1.8% following news of the measures, which will be a big blow in particular for Germany, where Europe's bigg Ford to cut European jobs as EV shift, Chinese rivals take ...

  9. Polynomial interpolation - Wikipedia

    en.wikipedia.org/wiki/Polynomial_interpolation

    Another example is the function f(x) = |x| on the interval [−1, 1], for which the interpolating polynomials do not even converge pointwise except at the three points x = ±1, 0. [ 13 ] One might think that better convergence properties may be obtained by choosing different interpolation nodes.