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  2. Nonlinear regression - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_regression

    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).

  3. Non-linear least squares - Wikipedia

    en.wikipedia.org/wiki/Non-linear_least_squares

    Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n). It is used in some forms of nonlinear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations.

  4. Nonlinear modelling - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_modelling

    The newer nonlinear modelling approaches include non-parametric methods, such as feedforward neural networks, kernel regression, multivariate splines, etc., which do not require a priori knowledge of the nonlinearities in the relations. Thus the nonlinear modelling can utilize production data or experimental results while taking into account ...

  5. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    For example, a simple univariate regression may propose (,) = +, suggesting that the researcher believes = + + to be a reasonable approximation for the statistical process generating the data. Once researchers determine their preferred statistical model , different forms of regression analysis provide tools to estimate the parameters β ...

  6. Least squares - Wikipedia

    en.wikipedia.org/wiki/Least_squares

    The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the ...

  7. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    The data at the extremes of x indicates that the relationship between y and x may be non-linear (look at the red dots relative to the regression line at low and high values of x). We thus turn to MARS to automatically build a model taking into account non-linearities. MARS software constructs a model from the given x and y as follows

  8. Thomas J. Falk - Pay Pals - The Huffington Post

    data.huffingtonpost.com/paypals/thomas-j-falk

    From June 2010 to December 2012, if you bought shares in companies when Thomas J. Falk joined the board, and sold them when he left, you would have a 18.2 percent return on your investment, compared to a 32.4 percent return from the S&P 500.

  9. Levenberg–Marquardt algorithm - Wikipedia

    en.wikipedia.org/wiki/Levenberg–Marquardt...

    The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters ⁠ ⁠ of the model curve (,) so that the sum of the squares of the deviations () is minimized: