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Differential geometric least angle regression: a differential geometric approach to sparse generalized linear models

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  • Luigi Augugliaro
  • Angelo M. Mineo
  • Ernst C. Wit

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  • Luigi Augugliaro & Angelo M. Mineo & Ernst C. Wit, 2013. "Differential geometric least angle regression: a differential geometric approach to sparse generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 471-498, June.
    • Handle: RePEc:bla:jorssb:v:75:y:2013:i:3:p:471-498
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    References listed on IDEAS

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    1. Li, Yehua & Wang, Naisyin & Carroll, Raymond J., 2010. "Generalized Functional Linear Models With Semiparametric Single-Index Interactions," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 621-633.
    2. Bo-Cheng Wei & Jian-Qing Shi & Wing-Kam Fung & Yue-Qing Hu, 1998. "Testing for Varying Dispersion in Exponential Family Nonlinear Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(2), pages 277-294, June.
    3. Gareth M. James & Peter Radchenko & Jinchi Lv, 2009. "DASSO: connections between the Dantzig selector and lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 127-142, January.
    4. Kato, Kengo, 2009. "On the degrees of freedom in shrinkage estimation," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1338-1352, August.
    5. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    6. Gareth M. James, 2002. "Generalized linear models with functional predictors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 411-432, August.
    7. Gareth M. James & Peter Radchenko, 2009. "A generalized Dantzig selector with shrinkage tuning," Biometrika, Biometrika Trust, vol. 96(2), pages 323-337.
    8. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    9. Bradley Efron, 2004. "The Estimation of Prediction Error: Covariance Penalties and Cross-Validation," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 619-632, January.
    10. Burbea, Jacob & Rao, C. Radhakrishna, 1982. "Entropy differential metric, distance and divergence measures in probability spaces: A unified approach," Journal of Multivariate Analysis, Elsevier, vol. 12(4), pages 575-596, December.
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    Cited by:

    1. Vinciotti Veronica & Augugliaro Luigi & Abbruzzo Antonino & Wit Ernst C., 2016. "Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(3), pages 193-212, June.
    2. Augugliaro, Luigi & Mineo, Angelo & Wit, Ernst C., 2014. "dglars: An R Package to Estimate Sparse Generalized Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i08).
    3. Pircalabelu, Eugen & Artemiou, Andreas, 2021. "Graph informed sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    4. Wit, Ernst C., 2018. "Big data and biostatistics: The death of the asymptotic Valhalla," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 30-33.

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