Tuesday, Aug 6: 2:00 PM - 3:50 PM
5122
Contributed Papers
Oregon Convention Center
Room: CC-G129
Main Sponsor
Section on Statistics and the Environment
Presentations
The link between Gaussian random fields and Markov random fields is well established based on a stochastic partial differential equation in Euclidean spaces, where the Matern covariance functions plan an essential role. We explore the extension of this link to circular spaces and uncover different results. It is known that Matern covariance functions are not always positive definite on the circle; however, the circular Matern covariance functions are shown to be valid on the circle and are the focus of this research. We show that the equivalence between the circular Matern random fields and Markov random fields is exact and this marks a departure from the Euclidean space counterpart, where only approximations are achieved. Moreover, the key motivation in Euclidean spaces for establishing such link relies on the assumptions that the corresponding Markov random field is sparse. We show that such sparsity does not hold in general on the circle. Additionally, we formally define the white noise space and its associated Brownian bridge on the circle for the stochastic differential equation used in this research.
Keywords
Conditional
autoregressive model
Circulant matrix
Abstracts
Low-dose radiation risks must be extrapolated from groups exposed at much higher levels of dose. Recently, there has been much attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings.
We assess regression calibration (RC), Monte Carlo maximum likelihood (MCML), frequentist model averaging (FMA), 2D Monte Carlo+Bayesian model averaging (2DMC+BMA) methods with simulated datasets having mixed Berkson and classical errors. We test all these against a modification of RC, the extended regression calibration (ERC) method. 2DMC+BMA has poor coverage for the dose coefficients; coverage of RC, MCML, FMA is better, although uniformly too high for FMA and MCML, and best for ERC. Bias in predicted relative risk is generally smallest for ERC, and largest for quasi-2DMC+BMA and FMA methods, with RC and MCML exhibiting bias in predicted risk somewhat intermediate between the other two methods. In summary, the new ERC method performs well in comparison to previously proposed methods and is particularly suited to situations with low to moderate amounts of shared and unshared Berkson errors.
Keywords
Covariate measurement error
Berkson error
Classical error
Regression calibration
Bayesian model averaging
Frequentist model averaging
Co-Author(s)
Nobuyuki Hamada, Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, CRIEPI
Lydia Zablotska, UCSF
First Author
Mark Little, Radiation Epidemiology Branch, National Cancer Institute
Presenting Author
Mark Little, Radiation Epidemiology Branch, National Cancer Institute
Sound is assumed to be the primary modality of communication among marine mammal species. Analyzing acoustic recordings helps to understand the function of the acoustic signals as well as the possible impact of anthropogenic noise on acoustic behavior. Motivated by a dataset from a network of hydrophones in Cape Cod Bay, Massachusetts, utilizing automatically detected calls in recordings, we study the communication process of the endangered North Atlantic right whale. For right whales an "up-call" is known as a contact call, and ensuing counter-calling between individuals is presumed to facilitate group cohesion. We present novel spatiotemporal excitement modeling consisting of a background process and a counter-call process. The background process intensity incorporates the influences of diel patterns and ambient noise on occurrence. The counter-call intensity captures potential excitement, that calling elicits calling behavior. Call incidence is found to be clustered in space and time; a call seems to excite more calls nearer to it in time and space. We find evidence that whales make more calls during twilight hours, respond to other whales nearby, and are likely to remain quiet in the presence of increased ambient noise.
Keywords
Gaussian process
Markov chain Monte Carlo
North Atlantic right whales
random time change theorem
spatial process
temporal point patterns
Abstracts
Scientists often collect samples on characteristics of different observation units and wonder whether the characteristics of the observation units have similar distributional structure. In this study, we propose a new Bayesian clustering method for distributions that uses a T-EP (Truncated Ewens-Pitman) distribution as the prior for the partitioning parameters, that is, for the number of clusters and the cluster sizes. For a given number of clusters, we consider an entropy-based objective function that is naturally derived from the modified Jensen-Shannon divergence between two distributions. This leads to a hierarchical Bayesian clustering method for distributions.
As a motivational example, we introduce yellowfin tuna fork length data collected from the tuna catch of purse-seine vessels that operated in the eastern Pacific Ocean. The hierarchical Bayesian clustering method, applied to density estimates of yellowfin tuna fork length for 5-degree square areas, was used to explore spatial structure in the length composition of the tuna catch.
Keywords
Hierarchical Bayesian model
Modified Jensen-Shannon divergence
Clustering for distributions
Truncated Ewens-Pitman distribution
Density estimates
Storm surge is the unusual rise in sea level caused by a storm's winds pushing water onshore. Due to high damage to roads, buildings, and lives, accurate estimation of storm surge high quantiles (e.g., r-year return level) and associated uncertainty are essential. Purely data-driven approaches to these tasks pose a challenge, as the number of hurricanes occurring near any single location is limited. A physically-driven approach, utilizing high-fidelity hydrodynamic computer simulations, provides an alternative by leveraging well-known physics to model how the surge level responds to hypothetical storms, where these simulated storms are parameterized by a few key characteristics. This approach requires the following tasks: 1) estimate the joint distribution of storm characteristics; 2) emulate the computer model input-output relationship via surrogate models; and 3) integrate out the input distribution to obtain the surge output distribution, then determine the synthetic data for high quantile (e.g. 1-in-100 year return level) estimation. A case study of this approach in South West Florida using the computer model ADCIRC will be presented to illustrate the proposed workflow.
Keywords
Gaussian Processes
Computer Model
Quantile Estimation
Hurricanes and Storm Surges
Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit their utility. For the newly published CESM2-LENS2 data, which suffer from this issue, we propose a novel stochastic generator (SG) as a practical complement to the CESM2, capable of rapidly producing emulations closely mirroring training simulations. Our SG leverages the spherical harmonic transformation to shift from spatial to spectral domains, enabling efficient low-rank approximations that significantly reduce computational and storage costs. By accounting for axial symmetry and retaining distinct ranks for land and ocean regions, our SG captures intricate non-stationary spatial dependencies. Additionally, a modified Tukey g-and-h transformation accommodates non-Gaussianity in high-temporal-resolution data. We apply the proposed SG to generate emulations for surface temperature simulations from the CESM2-LENS2 data across various scales, marking the first attempt of reproducing daily data. These emulations are then meticulously validated against training simulations.
Keywords
Emulator
Global temperature
Low-rank approximation
Non-stationary spatial structure
Tukey g-and-h transformation
Co-Author(s)
Zubair Khalid, School of Science and Engineering, Lahore University of Management Sciences
Marc Genton, King Abdullah University of Science and Technology
First Author
Yan Song
Presenting Author
Yan Song