mixregTrim {MixSemiRob}

Robust Regression Estimator Using Trimmed Likelihood

Description

‘mixregTrim’ is used for robust regression estimation of a mixture model using the trimmed likelihood estimator (Neykov et al., 2007). It trims the data to reduce the impact of outliers on the model.

Usage

mixregTrim(x, y, C = 2, keep = 0.95, nstart = 20)

Arguments

x

an n by p data matrix where n is the number of observations and p is the number of explanatory variables. The intercept term will automatically be added to the data.

y

an n-dimensional vector of response variable.

C

number of mixture components. Default is 2.

keep

proportion of data to be kept after trimming, ranging from 0 to 1. Default is 0.95.

nstart

number of initializations to try. Default is 20.

Value

A list containing the following elements:

pi

C-dimensional vector of estimated mixing proportions.

beta

C by (p + 1) matrix of estimated regression coefficients.

sigma

C-dimensional vector of estimated standard deviations.

lik

final likelihood.

References

Neykov, N., Filzmoser, P., Dimova, R., and Neytchev, P. (2007). Robust fitting of mixtures using the trimmed likelihood estimator. Computational Statistics & Data Analysis, 52(1), 299-308.

Examples

data(tone)
y = tone$tuned
x = tone$stretchratio
k = 160
x[151:k] = 0
y[151:k] = 5
est_TLE = mixregTrim(x, y, 2, 0.95, nstart = 1)