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Variable selection in functional linear concurrent regression

Author

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  • Rahul Ghosal
  • Arnab Maity
  • Timothy Clark
  • Stefano B. Longo

Abstract

We propose a novel method for variable selection in functional linear concurrent regression. Our research is motivated by a fisheries footprint study where the goal is to identify important time‐varying sociostructural drivers influencing patterns of seafood consumption, and hence the fisheries footprint, over time, as well as estimating their dynamic effects. We develop a variable‐selection method in functional linear concurrent regression extending the classically used scalar‐on‐scalar variable‐selection methods like the lasso, smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). We show that in functional linear concurrent regression the variable‐selection problem can be addressed as a group lasso, and their natural extension: the group SCAD or a group MCP problem. Through simulations, we illustrate that our method, particularly with the group SCAD or group MCP, can pick out the relevant variables with high accuracy and has minuscule false positive and false negative rate even when data are observed sparsely, are contaminated with noise and the error process is highly non‐stationary. We also demonstrate two real data applications of our method in studies of dietary calcium absorption and fisheries footprint in the selection of influential time‐varying covariates.

Suggested Citation

  • Rahul Ghosal & Arnab Maity & Timothy Clark & Stefano B. Longo, 2020. "Variable selection in functional linear concurrent regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 565-587, June.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:3:p:565-587
    DOI: 10.1111/rssc.12408
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    References listed on IDEAS

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    Cited by:

    1. Ghosal, Rahul & Maity, Arnab, 2022. "A Score Based Test for Functional Linear Concurrent Regression," Econometrics and Statistics, Elsevier, vol. 21(C), pages 114-130.
    2. Ghosal, Rahul & Matabuena, Marcos & Ghosh, Sujit K., 2025. "Functional time transformation model with applications to digital health," Computational Statistics & Data Analysis, Elsevier, vol. 207(C).
    3. Ghosal, Rahul & Ghosh, Sujit & Urbanek, Jacek & Schrack, Jennifer A. & Zipunnikov, Vadim, 2023. "Shape-constrained estimation in functional regression with Bernstein polynomials," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    4. Laura Freijeiro-González & Manuel Febrero-Bande & Wenceslao González-Manteiga, 2023. "Novel specification tests for synchronous additive concurrent model formulation based on martingale difference divergence," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 908-941, September.
    5. Ziesemer, Thomas, 2025. "Endogenous R&D elasticities of productivity functions and BERD bias," MERIT Working Papers 2025-017, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).

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