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37.5 Robust linear regression

Ordinary least squares (OLS) models are often heavily influenced by the presence of outliers. Outliers are data points which do not follow the general trend of the other observations, although there is strictly no precise definition of an outlier. Robust linear regression refers to regression algorithms which are robust to outliers. The most common type of robust regression is M-estimation. The general M-estimator minimizes the objective function

\sum_i \rho(e_i) = \sum_i \rho (y_i - Y(c, x_i))

where e_i = y_i - Y(c, x_i) is the residual of the ith data point, and \rho(e_i) is a function which should have the following properties:

The special case of ordinary least squares is given by \rho(e_i) = e_i^2. Letting \psi = \rho' be the derivative of \rho, differentiating the objective function with respect to the coefficients c and setting the partial derivatives to zero produces the system of equations

\sum_i \psi(e_i) X_i = 0

where X_i is a vector containing row i of the design matrix X. Next, we define a weight function w(e) = \psi(e)/e, and let w_i = w(e_i):

\sum_i w_i e_i X_i = 0

This system of equations is equivalent to solving a weighted ordinary least squares problem, minimizing \chi^2 = \sum_i w_i e_i^2. The weights however, depend on the residuals e_i, which depend on the coefficients c, which depend on the weights. Therefore, an iterative solution is used, called Iteratively Reweighted Least Squares (IRLS).

  1. Compute initial estimates of the coefficients c^{(0)} using ordinary least squares
  2. For iteration k, form the residuals e_i^{(k)} = (y_i - X_i c^{(k-1)})/(t \sigma^{(k)} \sqrt{1 - h_i}), where t is a tuning constant depending on the choice of \psi, and h_i are the statistical leverages (diagonal elements of the matrix X (X^T X)^{-1} X^T). Including t and h_i in the residual calculation has been shown to improve the convergence of the method. The residual standard deviation is approximated as \sigma^{(k)} = MAD / 0.6745, where MAD is the Median-Absolute-Deviation of the n-p largest residuals from the previous iteration.
  3. Compute new weights w_i^{(k)} = \psi(e_i^{(k)})/e_i^{(k)}.
  4. Compute new coefficients c^{(k)} by solving the weighted least squares problem with weights w_i^{(k)}.
  5. Steps 2 through 4 are iterated until the coefficients converge or until some maximum iteration limit is reached. Coefficients are tested for convergence using the critera:
    |c_i^(k) - c_i^(k-1)| \le \epsilon \times max(|c_i^(k)|, |c_i^(k-1)|)
    

    for all 0 \le i < p where \epsilon is a small tolerance factor.

The key to this method lies in selecting the function \psi(e_i) to assign smaller weights to large residuals, and larger weights to smaller residuals. As the iteration proceeds, outliers are assigned smaller and smaller weights, eventually having very little or no effect on the fitted model.

Function: gsl_multifit_robust_workspace * gsl_multifit_robust_alloc (const gsl_multifit_robust_type * T, const size_t n, const size_t p)

This function allocates a workspace for fitting a model to n observations using p parameters. The type T specifies the function \psi and can be selected from the following choices.

Robust type: gsl_multifit_robust_default

This specifies the gsl_multifit_robust_bisquare type (see below) and is a good general purpose choice for robust regression.

Robust type: gsl_multifit_robust_bisquare

This is Tukey’s biweight (bisquare) function and is a good general purpose choice for robust regression. The weight function is given by

w(e) = (1 - e^2)^2

and the default tuning constant is t = 4.685.

Robust type: gsl_multifit_robust_cauchy

This is Cauchy’s function, also known as the Lorentzian function. This function does not guarantee a unique solution, meaning different choices of the coefficient vector c could minimize the objective function. Therefore this option should be used with care. The weight function is given by

w(e) = 1 / (1 + e^2)

and the default tuning constant is t = 2.385.

Robust type: gsl_multifit_robust_fair

This is the fair \rho function, which guarantees a unique solution and has continuous derivatives to three orders. The weight function is given by

w(e) = 1 / (1 + |e|)

and the default tuning constant is t = 1.400.

Robust type: gsl_multifit_robust_huber

This specifies Huber’s \rho function, which is a parabola in the vicinity of zero and increases linearly for a given threshold |e| > t. This function is also considered an excellent general purpose robust estimator, however, occasional difficulties can be encountered due to the discontinuous first derivative of the \psi function. The weight function is given by

w(e) = 1/max(1,|e|)

and the default tuning constant is t = 1.345.

Robust type: gsl_multifit_robust_ols

This specifies the ordinary least squares solution, which can be useful for quickly checking the difference between the various robust and OLS solutions. The weight function is given by

w(e) = 1

and the default tuning constant is t = 1.

Robust type: gsl_multifit_robust_welsch

This specifies the Welsch function which can perform well in cases where the residuals have an exponential distribution. The weight function is given by

w(e) = \exp(-e^2)

and the default tuning constant is t = 2.985.

Function: void gsl_multifit_robust_free (gsl_multifit_robust_workspace * w)

This function frees the memory associated with the workspace w.

Function: const char * gsl_multifit_robust_name (const gsl_multifit_robust_workspace * w)

This function returns the name of the robust type T specified to gsl_multifit_robust_alloc.

Function: int gsl_multifit_robust_tune (const double tune, gsl_multifit_robust_workspace * w)

This function sets the tuning constant t used to adjust the residuals at each iteration to tune. Decreasing the tuning constant increases the downweight assigned to large residuals, while increasing the tuning constant decreases the downweight assigned to large residuals.

Function: int gsl_multifit_robust (const gsl_matrix * X, const gsl_vector * y, gsl_vector * c, gsl_matrix * cov, gsl_multifit_robust_workspace * w)

This function computes the best-fit parameters c of the model y = X c for the observations y and the matrix of predictor variables X, attemping to reduce the influence of outliers using the algorithm outlined above. The p-by-p variance-covariance matrix of the model parameters cov is estimated as \sigma^2 (X^T X)^{-1}, where \sigma is an approximation of the residual standard deviation using the theory of robust regression. Special care must be taken when estimating \sigma and other statistics such as R^2, and so these are computed internally and are available by calling the function gsl_multifit_robust_statistics.

Function: int gsl_multifit_robust_est (const gsl_vector * x, const gsl_vector * c, const gsl_matrix * cov, double * y, double * y_err)

This function uses the best-fit robust regression coefficients c and their covariance matrix cov to compute the fitted function value y and its standard deviation y_err for the model y = x.c at the point x.

Function: gsl_multifit_robust_stats gsl_multifit_robust_statistics (const gsl_multifit_robust_workspace * w)

This function returns a structure containing relevant statistics from a robust regression. The function gsl_multifit_robust must be called first to perform the regression and calculate these statistics. The returned gsl_multifit_robust_stats structure contains the following fields.


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