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

Description Usage Arguments Details Value Author(s) References

View source: R/leapp.R

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

1
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 yunting.sun@gmail.com, Nancy R.Zhang nzhang@stanford.edu, Art B.Owen owen@stanford.edu

References

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


leapp documentation built on May 2, 2019, 2:12 p.m.