Sieve-package: Nonparametric Estimation by the Method of Sieves

In Sieve: Nonparametric Estimation by the Method of Sieves

Nonparametric Estimation by the Method of Sieves

Description

Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: <arXiv:2206.02994> <arXiv:2104.00846><arXiv:2310.12140>.

Details

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~~ An overview of how to use the ~~ ~~ package, including the most ~~ ~~ important functions ~~

Author(s)

Tianyu Zhang

Maintainer: Tianyu Zhang <[e-mail]>

References

Tianyu Zhang and Noah Simon (2022) <arXiv:2206.02994>

Examples

xdim <- 5
basisN <- 1000
type <- 'cosine'

#non-linear additive truth. Half of the features are truly associated with the outcome
TrainData <- GenSamples(s.size = 300, xdim = xdim, 
            frho = 'additive', frho.para = xdim/2)

#noise-free testing samples
TestData <- GenSamples(s.size = 1e3, xdim = xdim, noise.para = 0, 
            frho = 'additive', frho.para = xdim/2)

sieve.model <- sieve_preprocess(X = TrainData[,2:(xdim+1)], 
            basisN = basisN, type = type, interaction_order = 2)

sieve.model <- sieve_solver(sieve.model, TrainData$Y, l1 = TRUE)

sieve_model_prediction <- sieve_predict(testX = TestData[,2:(xdim+1)], 
                testY = TestData$Y, sieve.model)

Sieve documentation built on Oct. 19, 2023, 5:12 p.m.