LassoSIR-package: Sparsed Sliced Inverse Regression via Lasso

In LassoSIR: Sparsed Sliced Inverse Regression via Lasso

Sparsed Sliced Inverse Regression via Lasso

Description

Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2019) <doi:10.1080/01621459.2018.1520115>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.

Details

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LassoSIR

Author(s)

Zhigen Zhao [aut, cre], Qian Lin [aut], Jun Liu [aut]

Maintainer: Zhigen Zhao <[e-mail]>

References

Lin, Q., Zhao, Z. , and Liu, J. (2018) On consistency and sparsity for sliced inverse regression in high dimension. Annals of Statistics. Vol. 46, Number 2. Page 580-610.

Lin, Q., Zhao, Z. , and Liu, J. (2019) Sparse Sliced Inverse Regression for High Dimensional Data. Journal of the American Statistical Association. Vol. 114, Number 528, Page 1726-1739.

See Also

NA

Examples

p <- 10
n <- 200


H <- 20
m <- n/H

beta <- array(0, c(p, 1) )
beta[1:3,1] <- rnorm(3, 0, 1)

X <- array(0, c(n, p ) )

rho <- 0.3
Sigma <- diag(p)
elements <- rho^(c((p-1):0,1:(p-1) ) )
for(i in 1:p)
    Sigma[i,] <- elements[(p+1-i):(2*p-i) ]


X <- matrix( rnorm(p*n), c(n, p) )
X <- X%*% chol(Sigma)

Y <-  ( X%*% beta )^3/2 + rnorm(n,0,1)
sir.lasso <- LassoSIR( X, Y, H, choosing.d="automatic",
          solution.path=FALSE, categorical=FALSE, nfolds=10,
          screening=FALSE)
beta.hat <- sir.lasso$beta/sqrt( sum( sir.lasso$beta^2 ) )

LassoSIR documentation built on April 3, 2025, 8:49 p.m.