ic.sglfit: Information criteria fit for sg-LASSO

In midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data

Information criteria fit for sg-LASSO

Description

Does information criteria for sg-LASSO regression model.

The function runs sglfit 1 time; computes the path solution in lambda sequence. Solutions for BIC, AIC and AICc information criteria are returned.

Usage

ic.sglfit(x, y, lambda = NULL, gamma = 1.0, gindex = 1:p,  ...)

Arguments

x	T by p data matrix, where T and p respectively denote the sample size and the number of regressors.
y	T by 1 response variable.
lambda	a user-supplied lambda sequence. By leaving this option unspecified (recommended), users can have the program compute its own λ sequence based on nlambda and lambda.factor. It is better to supply, if necessary, a decreasing sequence of lambda values than a single (small) value, as warm-starts are used in the optimization algorithm. The program will ensure that the user-supplied λ sequence is sorted in decreasing order before fitting the model.
gamma	sg-LASSO mixing parameter. γ = 1 gives LASSO solution and γ = 0 gives group LASSO solution.
gindex	p by 1 vector indicating group membership of each covariate.
...	Other arguments that can be passed to sglfit.

Details

The sequence of linear regression models implied by λ vector is fit by block coordinate-descent. The objective function is

||y - ια - xβ||²_T + 2λ Ω_γ(β),
where ι∈R^Tenter> and ||u||²_T=<u,u>/T is the empirical inner product. The penalty function Ω_γ(.) is applied on β coefficients and is

Ω_γ(β) = γ |β|₁ + (1-γ)|β|_2,1,
a convex combination of LASSO and group LASSO penalty functions.

Value

ic.sglfit object.

Author(s)

Jonas Striaukas

Examples

set.seed(1)
x = matrix(rnorm(100 * 20), 100, 20)
beta = c(5,4,3,2,1,rep(0, times = 15))
y = x%*%beta + rnorm(100)
gindex = sort(rep(1:4,times=5))
ic.sglfit(x = x, y = y, gindex = gindex, gamma = 0.5, 
  standardize = FALSE, intercept = FALSE)

midasml documentation built on April 29, 2022, 9:06 a.m.