# hdIS: Compute importance weights for lasso, group lasso, scaled... In EAlasso: Simulation Based Inference of Lasso Estimator

## Description

hdIS computes importance weights using samples drawn by PBsampler. See the examples below for details.

## Usage

 1 2 hdIS(PBsample, PETarget, sig2Target, lbdTarget, TsA.method = "default", log = TRUE, parallel = FALSE, ncores = 2L) 

## Arguments

 PBsample bootstrap samples of class PB from PBsampler. PETarget, sig2Target, lbdTarget parameters of target distribution. (point estimate of beta or E(y), estimated variance of error and lambda) TsA.method method to construct T(eta(s),A) matrix. See Zhou and Min(2016) for details. log logical. If log = TRUE, importance weight is computed in log scale. parallel logical. If parallel = TRUE, uses parallelization. Default is parallel = FALSE. ncores integer. The number of cores to use for parallelization.

## Details

computes importance weights which is defined as

\frac{target density}{proposal density}

, when the samples are drawn from the proposal distribution with the function PBsampler while the parameters of the target distribution are (PETarget, sig2Target, lbdTarget).

## Value

importance weights of the proposed samples.

## References

Zhou, Q. (2014), "Monte Carlo simulation for Lasso-type problems by estimator augmentation," Journal of the American Statistical Association, 109, 1495-1516.

Zhou, Q. and Min, S. (2017), "Estimator augmentation with applications in high-dimensional group inference," Electronic Journal of Statistics, 11(2), 3039-3080.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 set.seed(1234) n <- 10 p <- 30 Niter <- 10 Group <- rep(1:(p/10), each = 10) Weights <- rep(1, p/10) x <- matrix(rnorm(n*p), n) # Target distribution parameter PETarget <- rep(0, p) sig2Target <- .5 lbdTarget <- .37 # # Using non-mixture distribution # ------------------------------ ## Proposal distribution parameter PEProp1 <- rep(1, p) sig2Prop1 <- .5 lbdProp1 <- 1 PB <- PBsampler(X = x, PE_1 = PEProp1, sig2_1 = sig2Prop1, lbd_1 = lbdProp1, weights = Weights, group = Group, niter = Niter, type="grlasso", PEtype = "coeff") hdIS(PB, PETarget = PETarget, sig2Target = sig2Target, lbdTarget = lbdTarget, log = TRUE) # # Using mixture distribution # ------------------------------ # Target distribution parameters (coeff, sig2, lbd) = (rep(0,p), .5, .37) # Proposal distribution parameters # (coeff, sig2, lbd) = (rep(0,p), .5, .37) & (rep(1,p), 1, .5) # # PEProp1 <- rep(0,p); PEProp2 <- rep(1,p) sig2Prop1 <- .5; sig2Prop2 <- 1 lbdProp1 <- .37; lbdProp2 <- .5 PBMixture <- PBsampler(X = x, PE_1 = PEProp1, sig2_1 = sig2Prop1, lbd_1 = lbdProp1, PE_2 = PEProp2, sig2_2 = sig2Prop2, lbd_2 = lbdProp2, weights = Weights, group = Group, niter = Niter, type = "grlasso", PEtype = "coeff") hdIS(PBMixture, PETarget = PETarget, sig2Target = sig2Target, lbdTarget = lbdTarget, log = TRUE) 

EAlasso documentation built on Sept. 1, 2017, 9:03 a.m.