| Probabilistic Forecasting T Gneiting, M Katzfuss Annual Review of Statistics and Its Application 1 (1), 2014 | 959 | 2014 |
| 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 | 454* | 2018 |
| A multi-resolution approximation for massive spatial datasets M Katzfuss Journal of the American Statistical Association 112 (517), 201-214, 2017 | 266 | 2017 |
| Understanding the ensemble Kalman filter M Katzfuss, JR Stroud, CK Wikle The American Statistician 70 (4), 350-357, 2016 | 242 | 2016 |
| A general framework for Vecchia approximations of Gaussian processes M Katzfuss, J Guinness Statistical Science 36 (1), 124-141, 2021 | 240 | 2021 |
| 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 | 183 | 2011 |
| Bayesian hierarchical spatio‐temporal smoothing for very large datasets M Katzfuss, N Cressie Environmetrics 23 (1), 94-107, 2012 | 129 | 2012 |
| Spatio-temporal data fusion for very large remote sensing datasets H Nguyen, M Katzfuss, N Cressie, A Braverman Technometrics 56 (2), 174-185, 2014 | 102 | 2014 |
| Sparse Cholesky Factorization by Kullback--Leibler Minimization F Schäfer, M Katzfuss, H Owhadi SIAM Journal on Scientific Computing 43 (3), A2019-A2046, 2021 | 92 | 2021 |
| Vecchia approximations of Gaussian-process predictions M Katzfuss, J Guinness, W Gong, D Zilber Journal of Agricultural, Biological and Environmental Statistics, 1-32, 2020 | 88 | 2020 |
| Bayesian nonstationary spatial modeling for very large datasets M Katzfuss Environmetrics 24 (3), 189-200, 2013 | 85 | 2013 |
| 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 | 73 | 2019 |
| 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 | 70 | 2018 |
| 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 | 50 | 2011 |
| Constructing valid spatial processes on the sphere using kernel convolutions MJ Heaton, M Katzfuss, C Berrett, DW Nychka Environmetrics 25 (1), 2-15, 2014 | 44 | 2014 |
| Scaled Vecchia approximation for fast computer-model emulation M Katzfuss, J Guinness, E Lawrence SIAM/ASA Journal on Uncertainty Quantification 10 (2), 537-554, 2022 | 41 | 2022 |
| Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data D Zilber, M Katzfuss Computational Statistics & Data Analysis, 107081, 2020 | 41 | 2020 |
| GpGp: fast Gaussian process computation using Vecchia’s approximation J Guinness, M Katzfuss, Y Fahmy R package version 0.1. 0, 2018 | 41 | 2018 |
| Parallel inference for massive distributed spatial data using low-rank models M Katzfuss, D Hammerling Statistics and Computing 27 (2), 363-375, 2017 | 41 | 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 | 39 | 2017 |
Joe GuinnessCornell University在 cornell.edu 的电子邮件经过验证
