Envelope models and methods represent new constructions that can lead to substantial increases in estimation efficiency in multivariate analyses. The envlp toolbox implements a variety of envelope estimators under the framework of multivariate linear regression, including the envelope model, partial envelope model, heteroscedastic envelope model, inner envelope model, scaled envelope model, and envelope model in the predictor space. The toolbox also implements the envelope model for estimating a multivariate mean. The capabilities of this toolbox include estimation of the model parameters, as well as performing standard multivariate inference in the context of envelope models; for example, prediction and prediction errors, F test for two nested models, the standard errors for contrasts or linear combinations of coefficients, and more. Examples and datasets are contained in the toolbox to illustrate the use of each model. All functions and datasets are documented.
|Original language||English (US)|
|Number of pages||20|
|Journal||Journal of Statistical Software|
|State||Published - 2014|
Bibliographical notePublisher Copyright:
© 2015, American Statistical Association. All rights reserved.
- Dimension reduction
- Envelope models
- Grassmann manifold
- Multivariate linear regression