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  2. Residual (numerical analysis) - Wikipedia

    en.wikipedia.org/wiki/Residual_(numerical_analysis)

    Similar terminology is used dealing with differential, integral and functional equations.For the approximation of the solution of the equation () = (),the residual can either be the function

  3. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    A residual (or fitting ... If the data exhibit a trend, the regression model is likely incorrect; for example, the true function may be a quadratic or higher order ...

  4. Restricted maximum likelihood - Wikipedia

    en.wikipedia.org/wiki/Restricted_maximum_likelihood

    In statistics, the restricted (or residual, or reduced) maximum likelihood (REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data, so that nuisance parameters have no effect.

  5. Least absolute deviations - Wikipedia

    en.wikipedia.org/wiki/Least_absolute_deviations

    Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L 1 norm of such values.

  6. Residue (complex analysis) - Wikipedia

    en.wikipedia.org/wiki/Residue_(complex_analysis)

    This function appears to have a singularity at z = 0, but if one factorizes the denominator and thus writes the function as = ⁡ it is apparent that the singularity at z = 0 is a removable singularity and then the residue at z = 0 is therefore 0.

  7. List of numerical analysis topics - Wikipedia

    en.wikipedia.org/wiki/List_of_numerical_analysis...

    Linear-fractional programming — objective is ratio of linear functions, constraints are linear Fractional programming — objective is ratio of nonlinear functions, constraints are linear; Nonlinear complementarity problem (NCP) — find x such that x ≥ 0, f(x) ≥ 0 and x T f(x) = 0; Least squares — the objective function is a sum of squares