create_index_matrix

Create the index matrix for multivariate regression

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>.

Tianyu Zhang

Sieve

Nonparametric Estimation by the Method of Sieves

create_index_matrix function

<dl><dt>xdim</dt>
<dd>a number. It specifies the predictors' dimension.</dd>
<dt>basisN</dt>
<dd>a number. The number of basis function to use.</dd>
<dt>maxj</dt>
<dd>a number. We use this to specify the largest row product in the index list.</dd>
<dt>interaction_order</dt>
<dd>a number The maximum order of interaction. 1 means additive model, 2 means including pairwise interaction terms, etc.</dd></dl>

Arguments

Create the index matrix for multivariate regression — create_index_matrix

<dl>

<dt>xdim</dt>
<dd>a number. It specifies the predictors' dimension.</dd>


<dt>basisN</dt>
<dd>a number. The number of basis function to use.</dd>


<dt>maxj</dt>
<dd>a number. We use this to specify the largest row product in the index list.</dd>


<dt>interaction_order</dt>
<dd>a number The maximum order of interaction. 1 means additive model, 2 means including pairwise interaction terms, etc.</dd>

</dl>

create_index_matrix: Create the index matrix for multivariate regression

Description

Usage

Value

Arguments