IPODFUN: compute the iterative penalized outlier detection given the...

In leapp: Latent Effect Adjustment After Primary Projection

compute the iterative penalized outlier detection given the noise standard deviation sigma

Description

Y = X beta + gamma + sigma epsilon estimate k by 1 coefficients vector beta and N by 1 outlier indicator vector gamma from (Y,X).

Usage

IPODFUN(X, Y, H, sigma, betaInit, method = "hard", TOL = 1e-04)

Arguments

X	an N by k design matrix
Y	an N by 1 response vector
H	an N by N projection matrix X(X'X)^-1X'
sigma	a numeric, noise standard deviation
betaInit	a k by 1 initial value for coeffient beta
method	a string, if "hard", conduct hard thresholding, if "soft", conduct soft thresholding, default to "hard"
TOL	a numeric, tolerance of convergence, default to 1e-04

Details

The initial estimator for the coefficient beta can be chosen to be the estimator from a robust linear regression

Value

gamma	an N by 1 vector of estimated outlier indicator
ress	an N by 1 vector of residual Y - X beta - gamma

Author(s)

Yunting Sun [e-mail], Nancy R.Zhang [e-mail], Art B.Owen [e-mail]

References

She, Y. and Owen, A.B. "Outlier detection using nonconvex penalized regression" 2010

leapp documentation built on June 20, 2022, 1:05 a.m.