A5005838379- Soil Geostatistics and Mapping
- Climate variability and models
- Air Quality and Health Impacts
- Climate Change and Health Impacts
- Statistical Methods and Inference
- Hydrology and Watershed Management Studies
- Respiratory viral infections research
- Atmospheric and Environmental Gas Dynamics
- Meteorological Phenomena and Simulations
- Spatial and Panel Data Analysis
- Hydrology and Drought Analysis
- Gaussian Processes and Bayesian Inference
- Financial Risk and Volatility Modeling
- Remote Sensing in Agriculture
- Irrigation Practices and Water Management
- Data-Driven Disease Surveillance
- Remote Sensing and LiDAR Applications
- Data Management and Algorithms
- Viral Infections and Vectors
- Medical Imaging and Pathology Studies
- Traffic and Road Safety
- Bayesian Methods and Mixture Models
- Hydrology and Sediment Transport Processes
- Cryospheric studies and observations
- Animal Disease Management and Epidemiology
Brigham Young University
2015-2024
Hunter New England Local Health District
2023
Absynth Biologics (United Kingdom)
2020-2022
Michigan State University
2022
Simon Fraser University
2013
High Altitude Observatory
2013
NSF National Center for Atmospheric Research
2012-2013
Duke University
2010-2012
SOAS University of London
2009
University of Birmingham
2009
The Gaussian process is an indispensable tool for spatial data analysts. onset of the "big data" era, however, has lead to traditional being computationally infeasible modern data. As such, various alternatives full that are more amenable handling big have been proposed. These methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments facilitate computation. This study provides, first, introductory overview several analyzing large Second, this...
Modern digital data production methods, such as computer simulation and remote sensing, have vastly increased the size complexity of collected over spatial domains. Analysis these large datasets for scientific inquiry is typically carried out using Gaussian process. However, nonstationary behavior computational requirements can prohibit efficient implementation process models. To perform computationally feasible inference data, we consider partitioning a region into disjoint sets...
Remotely sensed data products are now routinely used to study various aspects of the Earth's atmosphere. These remote sensing datasets typically very high dimensional, have near global coverage and exhibit nonstationary spatial correlation structures. Proper statistical analysis these should be sufficiently flexible account for all aspects. To this end, we develop a kernel convolution construction processes on sphere. As is case with constructions plane, establish link between stationary...
The frequent use of antibiotics contributes to antibiotic resistance in bacteria, resulting an increase infections that are difficult treat. Livestock commonly administered their feed, but there is current interest raising animals only during active infections. Staphylococcus aureus (SA) a common pathogen both humans and livestock raised for human consumption. SA has achieved high levels resistance, the origins locations selection poorly understood. We determined prevalence MRSA conventional...
Extreme weather events such as thunderstorms and tornadoes are of great concern these pose a significant threat to life, property, economic stability. Because the difficulty gathering data on extreme events, this paper proposes modeling conditions for through large-scale indicators. The advantage using indicators is that climate models can be used generate whereas cannot themselves. This focuses comparing spatio-temporal reanalysis observed across continental United States Mexico. Results...
One common challenge of modeling intersection related crash data is the high proportion sites with zero crashes. Extensive research has been done on appropriate methods to handle excess zeroes. There some reluctance use zero-inflated models in traffic safety literature. The primary purpose this paper evaluate determine if they are a suitable method for counts. An approach model selection choose that best accomplishes objectives rather than attempting discover true underlying generating...
Abstract River flows change on timescales ranging from minutes to millennia. These vibrations in flow are tuned by diverse factors globally, for example, dams suppressing multi‐day variability or vegetation attenuating flood peaks some ecosystems. The relative importance of the physical, biological, and human influencing is an active area research, as related question finding a common language describing overall regime. Here, we addressed both topics using daily river discharge data set over...
Geomagnetic storms play a critical role in space weather physics with the potential for far reaching economic impacts including power grid outages, air traffic rerouting, satellite damage and GPS disruption. The LFM-MIX is state-of-the-art coupled magnetospheric-ionospheric model capable of simulating geomagnetic storms. Imbedded this are physical equations turning magnetohydrodynamic state parameters into energy flux electrons entering ionosphere, involving set input parameters. exact...
Understanding spatial and temporal dynamics of soil water within fields is critical for effective variable rate irrigation (VRI) management. The objectives this study were to develop VRI zones, manage rates examine differences in volumetric content (VWC) from events via sensors across zones. Five zones delineated after two years (2016 2017) yield evapotranspiration (ET) data collection. Soil placed each zone give real time VWC values assist decisions a 23 ha field winter wheat (Triticum...
Distributed lag (DL) models relate lagged covariates to a response and are popular statistical model used in wide variety of disciplines analyze exposure-response data.However, classical DL do not account for possible interactions between predictors.In the presence covariates, total effect change on is merely sum effects as typically assumed.This article proposes new class models, called high-degree that extend basic incorporate hypothesized predictors.The modeling strategy utilizes Gaussian...
A new estimation strategy for estimating the parameters of Heffernan and Tawn conditional extreme value model is proposed. The technique makes use empirical Bayes likelihood that otherwise does not have a simple closed‐form expression. approach tested on simulations from different types dependence (and independence) structures, as well two real data cases consisting precipitation analysis temperature in Boulder, Colorado, Los Angeles, California, USA. generally has good coverage when...
The Indus watershed is a highly populated region that contains parts of India, Pakistan, China, and Afghanistan. Changes in precipitation patterns rates glacial melt have significantly impacted the recent years, climate change projected to result further serious human environmental consequences. To understand dynamics surrounding regions, reanalysis satellite data from products such as APHRODITE-2, TRMM, ERA5, MERRA-2 are often used, yet these not always agreement regarding critical...
Reliable surveillance models are an important tool in public health because they aid mitigating disease outbreaks, identify where and when outbreaks occur, predict future occurrences. Although many statistical have been devised for purposes, none able to simultaneously achieve the practical goals of good sensitivity specificity, proper use covariate information, inclusion spatio‐temporal dynamics, transparent support decision‐makers. In effort these goals, this paper proposes a conditional...
The Florida Association of Pediatric Tumor Program (FAPTP) is a statewide network charged with the responsibility to monitor and evaluate children's cancer care in Florida. As part this responsibility, FAPTP collects data about race, gender, age, ZIP code tabulation area residence, year diagnosis for cases across In accord goals FAPTP, article seeks identify spatial, temporal, covariate regions rapid change rate occurrence goal understanding important spatial demographic factors that...
To compare imaging biomarkers from hyperpolarised 129Xe ventilation MRI and dynamic oxygen-enhanced (OE-MRI) with standard pulmonary function tests (PFT) in interstitial lung disease (ILD) patients. evaluate if can separate ILD subtypes detect early signs of resolution or progression. Prospective longitudinal. Forty-one (fourteen idiopathic fibrosis (IPF), eleven hypersensitivity pneumonitis (HP), drug-induced (DI-ILD), five connective tissue related-ILD (CTD-ILD)) patients ten healthy...
Multivariate receptor modeling is used to estimate profiles and contributions of pollution sources from concentrations pollutants such as particulate matter in the air. The majority previous approaches multivariate assume source are constant through time. In an effort relax this assumption, article uses Dirichlet distribution a dynamic linear model for profiles. developed herein evaluated using simulated datasets then applied physical dataset chemical species measured at U.S. Environmental...