Matthias Katzfuss
Matthias Katzfuss
Associate Professor of Statistics, Texas A&M University
Verified email at stat.tamu.edu - Homepage
Title
Cited by
Cited by
Year
Probabilistic Forecasting
T Gneiting, M Katzfuss
Annual Review of Statistics and Its Application 1 (1), 2014
4652014
A case study competition among methods for analyzing large spatial data
MJ Heaton, A Datta, AO Finley, R Furrer, J Guinness, R Guhaniyogi, ...
Journal of Agricultural, Biological and Environmental Statistics, 1-28, 2018
165*2018
A multi-resolution approximation for massive spatial datasets
M Katzfuss
Journal of the American Statistical Association 112 (517), 201-214, 2017
1522017
Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets
M Katzfuss, N Cressie
Journal of Time Series Analysis 32 (4), 430–446, 2011
1182011
Bayesian hierarchical spatio‐temporal smoothing for very large datasets
M Katzfuss, N Cressie
Environmetrics 23 (1), 94-107, 2012
882012
Understanding the ensemble Kalman filter
M Katzfuss, JR Stroud, CK Wikle
The American Statistician 70 (4), 350-357, 2016
742016
Bayesian nonstationary spatial modeling for very large datasets
M Katzfuss
Environmetrics 24 (3), 189-200, 2013
692013
A general framework for Vecchia approximations of Gaussian processes
M Katzfuss, J Guinness
arXiv preprint arXiv:1708.06302, 2017
602017
Spatio-temporal data fusion for very large remote sensing datasets
H Nguyen, M Katzfuss, N Cressie, A Braverman
Technometrics 56 (2), 174-185, 2014
602014
Spatio‐temporal models for large‐scale indicators of extreme weather
MJ Heaton, M Katzfuss, S Ramachandar, K Pedings, E Gilleland, ...
Environmetrics 22 (3), 294–303, 2011
382011
A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
JR Stroud, M Katzfuss, CK Wikle
Monthly Weather Review 146 (1), 373-386, 2018
342018
Maximum likelihood estimation of covariance parameters in the spatial-random-effects model
M Katzfuss, N Cressie
Proceedings of the Joint Statistical Meetings, 3378-3390, 2009
312009
Constructing valid spatial processes on the sphere using kernel convolutions
MJ Heaton, M Katzfuss, C Berrett, DW Nychka
Environmetrics 25 (1), 2-15, 2014
272014
Tutorial on Fixed Rank Kriging (FRK) of CO2 data
M Katzfuss, N Cressie
Technical Report No. 858, 2011
252011
Parallel inference for massive distributed spatial data using low-rank models
M Katzfuss, D Hammerling
Statistics and Computing 27 (2), 363-375, 2017
232017
Vecchia approximations of Gaussian-process predictions
M Katzfuss, J Guinness, W Gong, D Zilber
Journal of Agricultural, Biological and Environmental Statistics, 1-32, 2020
172020
Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models
M Katzfuss, JR Stroud, CK Wikle
Journal of the American Statistical Association, 1-68, 2019
17*2019
A class of multi-resolution approximations for large spatial datasets
M Katzfuss, W Gong
arXiv preprint arXiv:1710.08976, 2017
16*2017
A Bayesian hierarchical model for climate‐change detection and attribution
M Katzfuss, D Hammerling, RL Smith
Geophysical Research Letters 44 (11), 5720-5728, 2017
142017
Multi-resolution filters for massive spatio-temporal data
M Jurek, M Katzfuss
arXiv preprint arXiv:1810.04200, 2018
102018
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