Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Matthias Katzfuss"'
Autor:
Daniel Zilber, David R. Thompson, Matthias Katzfuss, Vijay Natraj, Jonathan Hobbs, Amy Braverman
Publikováno v:
Remote Sensing, Vol 14, Iss 9, p 2183 (2022)
Remote Visible/Shortwave Infrared (VSWIR) imaging spectroscopy is a powerful tool for measuring the composition of Earth’s surface over wide areas. This compositional information is captured by the spectral surface reflectance, where distinct shape
Externí odkaz:
https://doaj.org/article/c60ce25bceea4490b6ff9b06d2486816
Autor:
Jonathan Hobbs, Matthias Katzfuss, Daniel Zilber, Jenný Brynjarsdóttir, Anirban Mondal, Veronica Berrocal
Publikováno v:
Remote Sensing, Vol 13, Iss 4, p 571 (2021)
Modern remote-sensing retrievals often invoke a Bayesian approach to infer atmospheric properties from observed radiances. In this approach, plausible mean states and variability for the quantities of interest are encoded in a prior distribution. Rec
Externí odkaz:
https://doaj.org/article/9498ead5df15445b974d783084bcd1bd
Autor:
Jingjie Zhang, Matthias Katzfuss
Publikováno v:
Journal of Agricultural, Biological and Environmental Statistics. 27:440-460
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e6d866ab05745a8099d3e331be66591
https://doi.org/10.1007/s13253-022-00488-0
https://doi.org/10.1007/s13253-022-00488-0
Autor:
Anirban Chakraborty, Matthias Katzfuss
Publikováno v:
Journal of Data Science. :461-474
Spatio-temporal filtering is a common and challenging task in many environmental applications, where the evolution is often nonlinear and the dimension of the spatial state may be very high. We propose a scalable filtering approach based on a hierarc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c11e337ba616e32aa6544d873487b7fa
https://doi.org/10.6339/22-jds1071
https://doi.org/10.6339/22-jds1071
Autor:
Myeongjong Kang, Matthias Katzfuss
Publikováno v:
Statistics and Computing. 33
Gaussian processes are widely used as priors for unknown functions in statistics and machine learning. To achieve computationally feasible inference for large datasets, a popular approach is the Vecchia approximation, which is an ordered conditional
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22eec8e16911fe274488187998d46b07
https://doi.org/10.1007/s11222-023-10231-5
https://doi.org/10.1007/s11222-023-10231-5
The constellation of Earth-observing satellites now produces atmospheric greenhouse gas concentration estimates across multiple years. Their global coverage is providing additional information on the global carbon cycle. These products are combined w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::acd616cb04b729cf7b517828aea9d15f
https://gmd.copernicus.org/preprints/gmd-2022-230/
https://gmd.copernicus.org/preprints/gmd-2022-230/
Publikováno v:
Journal of Applied Econometrics. 36:728-743
Point forecasts can be interpreted as functionals (i.e., point summaries) of predictive distributions. We extend methodology for the identification of the functional based on time series of point forecasts and associated realizations. Focusing on sta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d4393fd199b2e56ae2289ea47a576b1
https://doi.org/10.1002/jae.2833
https://doi.org/10.1002/jae.2833
Autor:
Marcin Jurek, Matthias Katzfuss
Publikováno v:
Environmetrics. 34
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df50d94aba457db46a4bae3e4f1671aa
https://doi.org/10.1002/env.2757
https://doi.org/10.1002/env.2757
Autor:
Marcin Jurek, Matthias Katzfuss
Publikováno v:
Journal of Computational and Graphical Statistics. 30:1095-1110
Spatio-temporal data sets are rapidly growing in size. For example, environmental variables are measured with ever-higher resolution by increasing numbers of automated sensors mounted on satellites and aircraft. Using such data, which are typically n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1769b2bf73606e002cb5f002fb2c81da
https://doi.org/10.1080/10618600.2021.1886938
https://doi.org/10.1080/10618600.2021.1886938