Robust mean and covariance matrix using Huber-type weight

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Description

Robust mean and covariance matrix using Huber-type weight.

Usage

1
rsem.emmusig(xpattern, varphi=.1, max.it=1000, st='i')

Arguments

xpattern

Missing data pattern output from rsem.pattern.

varphi

Proportion of data to be down-weighted. Default is 0.1.

max.it

Maximum number of iterations for EM. Default is 1000

st

Starting values for EM algorithm. The default is 0 for mean and I for covariance. Alternative, the starting values can be estimated according to MCD.

Details

Estimate mean and covariance matrix using the expectation robust (ER) algorithm.

Value

err

Error code. 0: good. 1: maximum iterations are exceeded.

mu

Mean vector

sigma

Covariance matrix

weight

weight used in robust mean and covariance estimation.

Author(s)

Zhiyong Zhang and Ke-Hai Yuan

References

Yuan, K.-H., & Zhang, Z. (2012). Robust Structural Equation Modeling with Missing Data and Auxiliary Variables. Psychometrika, 77(4), 803-826.

See Also

rsem.emmusig