mixregLap {MixSemiRob}

Robust Mixture Regression with Laplace Distribution

Description

‘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).

Usage

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.

Value

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.

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.

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)