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  2. Mallows's Cp - Wikipedia

    en.wikipedia.org/wiki/Mallows's_Cp

    In statistics, Mallows's, [1] [2] named for Colin Lingwood Mallows, is used to assess the fit of a regression model that has been estimated using ordinary least squares.It is applied in the context of model selection, where a number of predictor variables are available for predicting some outcome, and the goal is to find the best model involving a subset of these predictors.

  3. Ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Ordinary_least_squares

    In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...

  4. Proofs involving ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Proofs_involving_ordinary...

    In general, the coefficients of the matrices , and can be complex. By using a Hermitian transpose instead of a simple transpose, it is possible to find a vector β ^ {\displaystyle {\boldsymbol {\widehat {\beta }}}} which minimizes S ( β ) {\displaystyle S({\boldsymbol {\beta }})} , just as for the real matrix case.

  5. Robust regression - Wikipedia

    en.wikipedia.org/wiki/Robust_regression

    The two regression lines are those estimated by ordinary least squares (OLS) and by robust MM-estimation. The analysis was performed in R using software made available by Venables and Ripley (2002). The two regression lines appear to be very similar (and this is not unusual in a data set of this size).

  6. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.

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  8. Seemingly unrelated regressions - Wikipedia

    en.wikipedia.org/wiki/Seemingly_unrelated...

    The model can be estimated equation-by-equation using standard ordinary least squares (OLS). Such estimates are consistent , however generally not as efficient as the SUR method, which amounts to feasible generalized least squares with a specific form of the variance-covariance matrix.

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