World's Best Scientists 2026 revealed!
Christopher K. Wikle

Christopher K. Wikle

D-Index & Metrics

Mathematics

D-Index
51
Citations
12798
World Ranking
1007
National Ranking
465

Engineering and Technology

D-Index
52
Citations
12742
World Ranking
3560
National Ranking
1046

Research.com Recognitions

  • 2004 - Fellow of the American Statistical Association (ASA)

Overview

Christopher K. Wikle is a researcher affiliated with the University of Missouri in the United States. Their work spans a range of topics primarily in environmental science and computer science, focusing on the intersection of statistical methods and complex spatial and spatiotemporal data.

The scientist has contributed significantly to the fields of environmental science, with 53 publications, and computer science, with 49 publications. Their research incorporates several subfields, including artificial intelligence, global and planetary change, environmental engineering, economics and econometrics, and ecology.

Their main research topics include:

  • Gaussian Processes and Bayesian Inference
  • Neural Networks and Applications
  • Model Reduction and Neural Networks
  • Atmospheric and Environmental Gas Dynamics
  • Demographic Modeling and Climate Adaptation
  • Time Series Analysis and Forecasting
  • Meteorological Phenomena and Simulations

Christopher K. Wikle has published several recent papers, including:

  • "Statistical Deep Learning for Spatial and Spatiotemporal Data," 2023, Annual Review of Statistics and Its Application
  • "An Illustration of Model Agnostic Explainability Methods Applied to Environmental Data," 2022, Environmetrics

Other influential papers among their recent works, although authored by different leading researchers in the field, are frequently referenced along with their own, reflecting the broader scientific context they operate within. These include:

  • "The ASA President's Task Force Statement on Statistical Significance and Replicability," 2021, The Annals of Applied Statistics
  • "Statistical Implementations of Agent-Based Demographic Models," 2020, International Statistical Review
  • "Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods," 2020, Trends in Ecology & Evolution

Their frequent publication venues are diverse, with a high number of publications in:

  • arXiv (Cornell University)
  • Environmetrics
  • Spatial Statistics
  • The Annals of Applied Statistics
  • Journal of Agricultural Biological and Environmental Statistics

Christopher K. Wikle has collaborated extensively with a number of researchers, notably including Erin M. Schliep, Scott H. Holan, Joshua S. North, Ranadeep Daw, and Jonathan R. Bradley.

In recognition of their contributions in statistics, they were named a Fellow of the American Statistical Association in 2004.

Best Publications

  • Statistics for Spatio-Temporal Data

    Noel A. C. Cressie;Christopher K. Wikle

  • Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

    Noel Cressie;Catherine A. Calder;James S. Clark;Jay M. Ver Hoef

  • A dimension-reduced approach to space-time Kalman filtering

    Christopher Wikle;Noel A Cressie

  • Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes

    Christopher K. Wikle

  • A Bayesian tutorial for data assimilation

    Christopher K. Wikle;L. Mark Berliner

  • Hierarchical Bayesian space-time models

    Christopher Wikle;L M Berliner;Noel A Cressie

  • Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds

    Christopher K Wikle;Ralph F Milliff;Doug Nychka;L Mark Berliner

  • Spatio-Temporal Statistics with R

    Christopher K. Wikle;Andrew Zammit-Mangion;Noel Cressie

  • Hierarchical Models in Environmental Science

    Christopher K. Wikle

  • Multiresolution models for nonstationary spatial covariance functions

    Douglas Nychka;Christopher Wikle;J Andrew Royle

  • Understanding the Ensemble Kalman Filter

    Matthias Katzfuss;Jonathan R. Stroud;Christopher K. Wikle

  • A general science-based framework for dynamical spatio-temporal models

    Christopher K. Wikle;Mevin B. Hooten

  • Long-Lead Prediction of Pacific SSTs via Bayesian Dynamic Modeling

    L. Mark Berliner;Christopher K. Wikle;Noel Cressie

  • A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

    Mevin B. Hooten;Christopher K. Wikle

  • Low-Rank Representations for Spatial Processes

    Christopher K. Wikle

  • A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities

    Ke Xu;Christopher K Wikle;Neil I Fox

  • A kernel-based spectral model for non-Gaussian spatio-temporal processes:

    Christopher K Wikle

  • Space-time Kalman filter

    Noel A Cressie;Christopher K Wikle

  • Population Influences on Tornado Reports in the United States

    Christopher J. Anderson;Christopher K. Wikle;Qin Zhou;J. Andrew Royle

  • Combining Information Across Spatial Scales

    Christopher K Wikle;L. Mark Berliner

  • Modeling Hydrologic Change: Statistical Methods

    Christopher K Wikle

Frequent Co-Authors

Noel A Cressie
Noel A Cressie University of Wollongong
Mevin B. Hooten
Mevin B. Hooten The University of Texas at Austin
J. Andrew Royle
J. Andrew Royle United States Geological Survey
Joshua J. Millspaugh
Joshua J. Millspaugh University of Montana
Douglas Nychka
Douglas Nychka Colorado School of Mines
Håvard Rue
Håvard Rue King Abdullah University of Science and Technology
Nadia Pinardi
Nadia Pinardi University of Bologna
Tsing-Chang Chen
Tsing-Chang Chen Iowa State University
Andrew M. Moore
Andrew M. Moore University of California, Santa Cruz
Thomas M. Powell
Thomas M. Powell University of California, Berkeley

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Mathematics in the USA, exploring related online degrees can open diverse career pathways. A master's degree in marketing offers a great blend of analytical skills and business acumen, useful for roles in data-driven marketing and strategy.

For those aiming to fast-track their education, 12 month MBA programs provide an intensive learning experience suitable for mathematicians looking to transition into leadership or entrepreneurial roles quickly.

Flexibility is key when advancing your education, and knowing can you transfer MBA programs can help learners capitalize on prior coursework, making it easier to customize their learning journey without losing credits.

Lastly, the surge in demand for data specialists makes the best masters in data analytics programs a crucial option for mathematicians. These programs focus on extracting insights from complex data sets, aligning well with mathematical expertise.

Best Scientists Citing Christopher K. Wikle

Trending Scientists