mixregBisq: Robust EM Algorithm For Mixture of Linear Regression Based on...

In MixSemiRob: Mixture Models: Parametric, Semiparametric, and Robust

Robust EM Algorithm For Mixture of Linear Regression Based on Bisquare Function

Description

‘mixregBisq’ is used to robustly estimate the parameters of a mixture regression model using the bisquare function based on multiple initial values (Bai et al., 2012). The solution is the mode of the solutions obtained from all initial values.

Usage

mixregBisq(x, y, C = 2, 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.
nstart	number of initializations to try. Default is 20.

Value

A list containing the following element:

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.

References

Bai, X., Yao, W., and Boyer, J. E. (2012). Robust fitting of mixture regression models. Computational Statistics & Data Analysis, 56(7), 2347-2359.

Examples

data(tone)
y = tone$tuned
x = tone$stretchratio
k = 160
x[151:k] = 0
y[151:k] = 5
est_bi = mixregBisq(x, y, 2, nstart = 20)

MixSemiRob documentation built on Sept. 20, 2023, 9:06 a.m.