ao: Approximated Octagon Penalty

Description Usage Arguments Details Value Author(s) References See Also

View source: R/ao.R

Description

Object of the penalty class to handle the AO penalty (Ulbricht, 2010).

Usage

1
ao (lambda = NULL, ...)

Arguments

lambda

two dimensional tuning parameter parameter. The first component corresponds to the regularization parameter λ. This must be a nonnegative real number. The second component indicates the exponent γ of the bridge penalty term. See details below. It must hold that γ > 1.

...

further arguments.

Details

The basic idea of the AO penalty is to use a linear combination of L_1-norm and the bridge penalty with γ > 1 where the amount of the bridge penalty part is driven by empirical correlation. So, consider the penalty

P_{\tilde{λ}}^{ao}(\boldsymbol{β}) = ∑_{i = 2}^p ∑_{j< i} p_{\tilde{λ},ij} (\boldsymbol{β}), \quad \tilde{λ} = (λ, γ)

where

p_{\tilde{λ},ij} = λ[(1 - |\varrho_{ij}|) (|β_i| + |β_j|) + |\varrho_{ij}|(|β_i|^γ + |β_j|^γ)],

and \varrho_{ij} denotes the value of the (empirical) correlation of the i-th and j-th regressor. Since we are going to approximate an octagonal polytope in two dimensions, we will refer to this penalty as approximated octagon (AO) penalty. Note that P_{\tilde{λ}}^{ao}(\boldsymbol{β}) leads to a dominating lasso term if the regressors are uncorrelated and to a dominating bridge term if they are nearly perfectly correlated.

The penalty can be rearranged as

P_{\tilde{λ}}^{ao}(\boldsymbol{β}) = ∑_{i=1}^p p_{\tilde{λ},i}^{ao}(β_i),

where

p_{\tilde{λ},i}^{ao}(β_i) = λ ≤ft\{|β_i|∑_{j \neq i} (1 - |\varrho_{ij}|) + |β_i|^γ ∑_{j \neq i} |\varrho_{ij}|\right\}.

It uses two tuning parameters \tilde{λ} = (λ, γ), where λ controls the penalty amount and γ manages the approximation of the pairwise L_∞-norm.

Value

An object of the class penalty. This is a list with elements

penalty

character: the penalty name.

lambda

double: the (nonnegative) regularization parameter.

getpenmat

function: computes the diagonal penalty matrix.

Author(s)

Jan Ulbricht

References

Ulbricht, Jan (2010) Variable Selection in Generalized Linear Models. Ph.D. Thesis. LMU Munich.

See Also

penalty, genet


lqa documentation built on May 30, 2017, 3:41 a.m.

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