ramml: Robust Adaptive Modified Maximum Likelihood

In rpls: Robust Partial Least Squares

Description

Modified Maximum Likelihood (MML) estimators are asymptotically equivalent to the ML estimators but their methodology works under the assumption of a known shape parameter. Robust Adaptive MML estimators weaken this assumption and are robust to vertical outliers as well as leverage points.

Usage

ramml(X,y,p,e)

Arguments

X	predictor matrix
y	response variable
p	shape parameter of long-tailed symmetric distribution (considered as robustness tuning constant)
e	parameter for the linearization of the intractable term

Value

coef	vector of coefficients
scale	estimate of sigma
fitted.values	vector with fitted y-values
residuals	vector with y-residuals

Author(s)

Sukru Acitas <[e-mail]>

References

S. Acitas, Robust Statistical Estimation Methods for High-Dimensional Data with Applications, tech. rep., TUBITAK 2219, International Post Doctoral Research Fellowship Programme, 2019.

rpls documentation built on July 8, 2020, 6:46 p.m.