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envlp: A MATLAB Toolbox for Computing Envelope Estimators in Multivariate Analysis

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  • Cook, R. Dennis
  • Su, Zhihua
  • Yang, Yi

Abstract

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.

Suggested Citation

  • Cook, R. Dennis & Su, Zhihua & Yang, Yi, 2015. "envlp: A MATLAB Toolbox for Computing Envelope Estimators in Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i08).
    • Handle: RePEc:jss:jstsof:v:062:i08
    DOI: http://hdl.handle.net/10.18637/jss.v062.i08
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    References listed on IDEAS

    as
    1. Zhihua Su & R. Dennis Cook, 2012. "Inner envelopes: efficient estimation in multivariate linear regression," Biometrika, Biometrika Trust, vol. 99(3), pages 687-702.
    2. Zhihua Su & R. Dennis Cook, 2011. "Partial envelopes for efficient estimation in multivariate linear regression," Biometrika, Biometrika Trust, vol. 98(1), pages 133-146.
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    Cited by:

    1. Minji Lee & Zhihua Su, 2020. "A Review of Envelope Models," International Statistical Review, International Statistical Institute, vol. 88(3), pages 658-676, December.
    2. Lan Liu & Wei Li & Zhihua Su & Dennis Cook & Luca Vizioli & Essa Yacoub, 2022. "Efficient estimation via envelope chain in magnetic resonance imaging‐based studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 481-501, June.

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