Abstract In this paper, we present counterexamples to maximal$$L^p$$ -regularity for a parabolic PDE. The example is a second-order operator in divergence form with space and time-dependent coefficients. It is well-known from Lions’ theory that such operators admit maximal$$L^2$$ -regularity on$$H^{-1}$$ under a coercivity condition on the coefficients, and without any regularity conditions in time and space. We show that in general one cannot expect maximal$$L^p$$ -regularity on$$H^{-1}(\mathbb {R}^d)$$ or$$L^2$$ -regularity on$$L^2(\mathbb {R}^d)$$ .
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Shape-based functional data analysis
Abstract Functional data analysis (FDA) is a fast-growing area of research and development in statistics. While most FDA literature imposes the classical$$\mathbb {L}^2$$ Hilbert structure on function spaces, there is an emergent need for a different, shape-based approach for analyzing functional data. This paper reviews and develops fundamental geometrical concepts that help connect traditionally diverse fields of shape and functional analyses. It showcases that focusing on shapes is often more appropriate when structural features (number of peaks and valleys and their heights) carry salient information in data. It recaps recent mathematical representations and associated procedures for comparing, summarizing, and testing the shapes of functions. Specifically, it discusses three tasks: shape fitting, shape fPCA, and shape regression models. The latter refers to the models that separate the shapes of functions from their phases and use them individually in regression analysis. The ensuing results provide better interpretations and tend to preserve geometric structures. The paper also discusses an extension where the functions are not real-valued but manifold-valued. The article presents several examples of this shape-centric functional data analysis using simulated and real data.
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- Award ID(s):
- 1953087
- PAR ID:
- 10444666
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- TEST
- Volume:
- 33
- Issue:
- 1
- ISSN:
- 1133-0686
- Format(s):
- Medium: X Size: p. 1-47
- Size(s):
- p. 1-47
- Sponsoring Org:
- National Science Foundation
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