Description Usage Arguments Details Value References
This function computes a matrix A and a vector b, such that outlier detection using lasso is equivalent to A y ≥ b.
1 | constrInResponseLasso(n, p, PXperp, outlier.det, outlier.det.sign, cutoff)
|
n, |
the number of observations. |
p, |
the number of variables, including the intercept. |
PXperp, |
the projection matrix onto the orthogonal complement of the column space of the design matrix X. |
outlier.det, |
indexes of detected outliers, can be empty. |
outlier.det.sign, |
the sign of the active variable estimated by lasso. |
cutoff, |
the cutoff λ (see details). |
Consider solving the following program
minimize ||y-Xβ-u||_2^2/(2n) + λ ||u||_1.
The i-th observation is considered as an outlier if and only if \hat u_i \neq 0. This is equivalent to solving
minimize ||P_X^\perp (y-u)||_2^2/(2n) + λ ||u||_1.
Then the variable selection can be characterized by a set of affine constraints
Ay ≥ b. In essence, this function is equivalent to
selectiveInference:::fixedLasso.poly
, but adapted to our notations
and up to some scaling factors and linear transformations.
This function returns a list (A, b).
Lee, Jason D., et al. "Exact post-selection inference, with application to the lasso." The Annals of Statistics 44.3 (2016): 907-927.
Tibshirani, R., et al. "selectiveInference: Tools for Post-Selection Inference." R package version 1.3 (2016).
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