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Solution paths for the generalized lasso with applications to spatially varying coefficients regression

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  • Zhao, Yaqing
  • Bondell, Howard

Abstract

Penalized regression can improve prediction accuracy and reduce dimension. The generalized lasso problem is used in many applications in various fields. The generalized lasso penalizes a linear transformation of the coefficients rather than the coefficients themselves. The proposed algorithm solves the generalized lasso problem and provides the full solution path. A confidence set can then be constructed on the generalized lasso parameters based on the modified residual bootstrap lasso. The approach is demonstrated using spatially varying coefficients regression, and it is shown to be both accurate and efficient compared to previous work.

Suggested Citation

  • Zhao, Yaqing & Bondell, Howard, 2020. "Solution paths for the generalized lasso with applications to spatially varying coefficients regression," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:csdana:v:142:y:2020:i:c:s0167947319301689
    DOI: 10.1016/j.csda.2019.106821
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    Cited by:

    1. Yuan Yan & Hsin-Cheng Huang & Marc G. Genton, 2021. "Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 387-408, September.

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