Large Spatial Data Modeling and Analysis: A Krylov Subspace Approach
You are welcome to attend the following Statistics and Stochastic colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe University.
- Thursday 15 October 2020 12:00 pm (Add to calendar)
- Andriy Olenko
- Presented by:
- Dr Tingjin Chu, University of Melbourne
- Type of Event:
Abstract: Estimating the parameters of spatial models for large spatial datasets can be computationally challenging, as it involves repeated evaluation of sizable spatial covariance matrices. In this paper, we aim to develop Krylov subspace based methods that are computationally efficient for large spatial data. Specifically, we approximate the inverse and the log-determinant of the spatial covariance matrix in the log-likelihood function via conjugate gradient and stochastic Lanczos on a Krylov subspace. These methods reduce the computational complexity from $O(N^3)$ to $O(N^2)$ and $O(N\log N)$ for dense and sparse matrices, respectively. Moreover, we quantify the difference between the approximated log-likelihood function and the original log-likelihood function and establish the consistency of parameter estimates. Simulation studies are conducted to examine the computational efficiency as well as the finite-sample properties. For illustration, our methodology is applied to analyze a large LiDAR dataset.
This is joint work with Jialuo Liu, Jun Zhu and Haonan Wang.
ZOOM LINK: https://latrobe.zoom.us/j/98357628534
Other events by type
- Classroom teaching for mixed mode cohorts
20th Jan 2021 12:30pm
- LMS design for effective mixed mode learning
20th Jan 2021 2:30pm
- HIV and Hepatitis Pre and Post Test Discussion - A short course for infection prevention and control practitioners
11th Feb 2021
- Understanding the impediments to uptake and diffusion of take-home naloxone in Australia: Report launch and findings from a large qualitative study
17th Feb 2021 4:00pm
- Responding to disclosures (webinar)
5th May 2021 10:00am