Daniel R. Kowal, Antonio Canale
arXiv 4 Aug 2021 · Statistics — Methodology · publishedBayesian Analysis (2023) · 15 citations (OpenAlex)
arXiv:2108.02151 · PDF · DOI · OpenAlex · Extracted main text
Functional data are frequently accompanied by a parametric template that describes the typical shapes of the functions. However, these parametric templates can incur significant bias, which undermines both utility and interpretability. To correct for model misspecification, we augment the parametric template with an infinite-dimensional nonparametric functional basis. The nonparametric basis functions are learned from the data and constrained to be orthogonal to the parametric template, which preserves distinctness between the parametric and nonparametric terms. This distinctness is essential to prevent functional confounding, which otherwise induces severe bias for the parametric terms. The nonparametric factors are regularized with an ordered spike-and-slab prior that provides consistent rank selection and satisfies several appealing theoretical properties. The versatility of the proposed approach is illustrated through applications to synthetic data, human motor control data, and dynamic yield curve data. Relative to parametric and semiparametric alternatives, the proposed semiparametric functional factor model eliminates bias, reduces excessive posterior and predictive uncertainty, and provides reliable inference on the effective number of nonparametric terms--all with minimal additional computational costs.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nelson, C. R. and Siegel, A. F (1987) Parsimonious Modeling of Yield Curves | 1.000 | 9 | 4 | 100% |
| 2 | Kowal, D. R (2020) Dynamic Regression Models for Time-Ordered Functional Data self | 1.000 | 5 | 4 | 100% |
| 3 | Diebold, F. X. and Li, C (2006) Forecasting the term structure of government bond yields | 0.928 | 4 | 3 | 100% |
| 4 | Castillo, I. and van der Vaart, A (2012) Needles and straw in a haystack: Posterior concentration for possibly sparse sequences | 0.811 | 4 | 2 | 100% |
| 5 | Teh, Y. W., Grür, D., and Ghahramani, Z (2007) Stick-breaking construction for the Indian buffet process | 0.737 | 3 | 2 | 100% |
| 6 | Legramanti, S., Durante, D., and Dunson, D. B (2020) Bayesian cumulative shrinkage for infinite factorizations | 0.693 | 8 | 1 | 100% |
| 7 | Ramsay, J. O., Wang, X., and Flanagan, R (1995) A functional data analysis of the pinch force of human fingers | 0.644 | 4 | 1 | 100% |
| 8 | Ramsay, J. O (2000) Functional components of variation in handwriting | 0.644 | 2 | 2 | 100% |
| 9 | Goldsmith, J. and Kitago, T (2016) Assessing systematic effects of stroke on motor control by using hierarchical function-on-scalar regression | 0.644 | 2 | 2 | 100% |
| 10 | Rockova, V (2018) Bayesian estimation of sparse signals with a continuous spike and slab prior | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 35 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Generalized Cumulative Shrinkage Process Priors with Applications to Sparse Bayesian Factor Analysis | 1.000 | 24 | 5 |
| 2 | Approximate Factor Models for Functional Time Series | 0.405 | 1 | 1 |