mixregLap function

Robust Mixture Regression with Laplace Distribution

`mixregLap' provides robust estimation for a mixture of linear regression models by assuming that the error terms follow the Laplace distribution (Song et al., 2014).

mixregLap(x, y, C = 2, nstart = 20, tol = 1e-05)

Arguments

• x: an n by p matrix of observations (one observation per row). The intercept will be automatically added to x.
• y: an n-dimensional vector of response variable.
• C: number of mixture components. Default is 2.
• nstart: number of initializations to try. Default is 20.
• tol: stopping criteria (threshold value) for the EM algorithm. Default is 1e-05.

Returns

A list containing the following elements: - beta: C by (p + 1) matrix of estimated regression coefficients.

• sigma: C-dimensional vector of estimated component standard deviations.

• pi: C-dimensional vector of estimated mixing proportions.

• lik: final likelihood.

• run: total number of iterations after convergence.

Examples

data(tone)
y = tone$tuned          # length(y) = 160
x = tone$stretchratio   # length(x) = 160
k = 160
x[151:k] = 0
y[151:k] = 5
est_lap = mixregLap(x, y, 2)

References

Song, W., Yao, W., and Xing, Y. (2014). Robust mixture regression model fitting by Laplace distribution. Computational Statistics & Data Analysis, 71, 128-137.

See Also

mixregT for robust estimation with t-distribution.