ridge: Outlier detection with a ridge penalty

In leapp: Latent Effect Adjustment After Primary Projection

Outlier detection with a ridge penalty

Description

Outlier detection and robust regression with a ridge type penalty on the outlier indicator gamma. Allow non sparse outliers and require known noise standard deviation.

Usage

ridge(X, Y, H, sigma)

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)^{-1}X'
sigma	a numeric, noise standard deviation

Value

p	an N by 1 vector of p-values for each of the N genes
gamma	an N by 1 vector of estimated primary variable gamma

Author(s)

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

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