Advanced Nonparametric Methods for Function Derivatives and Related Localized Features
| dc.contributor.advisor | Li, Meng | |
| dc.creator | Liu, Zejian | |
| dc.date.accessioned | 2024-05-21T21:47:21Z | |
| dc.date.created | 2024-05 | |
| dc.date.issued | 2024-04-15 | |
| dc.date.submitted | May 2024 | |
| dc.date.updated | 2024-05-21T21:47:21Z | |
| dc.description | EMBARGO NOTE: This item is embargoed until 2024-11-01 | |
| dc.description.abstract | In this thesis, we propose nonparametric statistical methods to study the derivatives and localized features of functions, areas of paramount importance across diverse scientific disciplines such as cosmology, environmental science, and neuroscience. We first develop a plug-in kernel ridge regression estimator for derivatives of arbitrary order. We provide both non-asymptotic and asymptotic error bounds for this estimator and show its substantial improvements over existing techniques, particularly for high-order derivatives and multi-dimensional settings. We then transition to exploring the application of Gaussian processes (GPs) in modeling derivative functionals, addressing a long-standing skepticism surrounding derivative estimation strategies using GPs. We show that the GP prior exhibits a remarkable plug-in property with minimax optimality---this, to the best of our knowledge, provides the first positive result for using GPs in estimating function derivatives. A data-driven empirical Bayes approach is studied for hyperparameter tuning, which achieves theoretical optimality and computational efficiency. Lastly, we tackle the challenging problem of identifying and analyzing localized features of functions, such as local extrema, in the presence of noise. To this end, we introduce a semiparametric Bayesian method built upon derivative-constrained GP priors. We establish large sample frequentist properties for the proposed method, from which point and interval estimates are derived, offering the advantage of fast implementation without requiring complex sampling procedures. Overall, this thesis blends theoretical and methodological innovation with practical application, aiming to equip researchers and practitioners with more powerful and versatile tools for the analysis of nonparametric functions. The advancements presented herein not only contribute to the statistical community but also have broad implications across various fields where derivative estimation and localized feature analysis are essential. Our methods are illustrated via a climate change application for analyzing the global sea-level rise, and an application to cognitive science for analyzing event-related potentials. | |
| dc.embargo.lift | 2024-11-01 | |
| dc.embargo.terms | 2024-11-01 | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Liu, Zejian. Advanced Nonparametric Methods for Function Derivatives and Related Localized Features. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/116130 | |
| dc.identifier.uri | https://hdl.handle.net/1911/116130 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | Derivative estimation | |
| dc.subject | Gaussian process | |
| dc.subject | Kernel ridge regression | |
| dc.subject | Local extrema | |
| dc.subject | Nonparametric statistics | |
| dc.subject | Plug-in property | |
| dc.title | Advanced Nonparametric Methods for Function Derivatives and Related Localized Features | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Statistics | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |