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Projection pursuit multi-index (PPMI) models

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  • Akritas, Michael G.

Abstract

The concept of joint projective directions and the class of projection pursuit multi-index (PPMI) models are introduced. PPMI models are MI models with hierarchically defined directions spanning the central mean subspace, and bridge the gap between PP and MI models.

Suggested Citation

  • Akritas, Michael G., 2016. "Projection pursuit multi-index (PPMI) models," Statistics & Probability Letters, Elsevier, vol. 114(C), pages 99-103.
    • Handle: RePEc:eee:stapro:v:114:y:2016:i:c:p:99-103
    DOI: 10.1016/j.spl.2016.03.008
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    References listed on IDEAS

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    1. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    2. Xia, Yingcun, 2008. "A Multiple-Index Model and Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1631-1640.
    3. Stoker, Thomas M, 1986. "Consistent Estimation of Scaled Coefficients," Econometrica, Econometric Society, vol. 54(6), pages 1461-1481, November.
    4. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
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    Cited by:

    1. Jiang Zhu & Zhenyu Zhao, 2017. "Chinese Electric Power Development Coordination Analysis on Resource, Production and Consumption: A Provincial Case Study," Sustainability, MDPI, vol. 9(2), pages 1-19, February.

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