Zobrazeno 1 - 10
of 479
pro vyhledávání: '"model diagnostics"'
Publikováno v:
IEEE Access, Vol 12, Pp 42385-42400 (2024)
Markov Chain Monte Carlo (MCMC) is a robust statistical approach for estimating posterior distributions. However, the significant computational cost associated with MCMC presents a considerable challenge, complicating the selection of an appropriate
Externí odkaz:
https://doaj.org/article/a467c4989d474bc7986e828720685875
Akademický článek
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Akademický článek
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Publikováno v:
Water Quality Research Journal, Vol 57, Iss 1, Pp 40-57 (2022)
Robust scientific inference is crucial to ensure evidence-based decision making. Accordingly, the selection of appropriate statistical tools and experimental designs is integral to achieve accuracy from data analytical processes. Environmental monito
Externí odkaz:
https://doaj.org/article/5b19bfd5c6fe4a29a020a11ac86dcb5a
Publikováno v:
Atmospheric Science Letters, Vol 24, Iss 4, Pp n/a-n/a (2023)
Abstract Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data
Externí odkaz:
https://doaj.org/article/1a6941c14cb641419b7e5ea9093b657b
Autor:
Adam Loy
Publikováno v:
Journal of Statistics and Data Science Education, Vol 29, Iss 2, Pp 171-182 (2021)
In the classroom, we traditionally visualize inferential concepts using static graphics or interactive apps. For example, there is a long history of using apps to visualize sampling distributions. The lineup protocol for visual inference is a recent
Externí odkaz:
https://doaj.org/article/f0216b5934eb487fb262001da0148a17
Autor:
Edom Moges, Benjamin L. Ruddell, Liang Zhang, Jessica M. Driscoll, Parker Norton, Fernando Perez, Laurel G. Larsen
Publikováno v:
Frontiers in Earth Science, Vol 10 (2022)
Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and di
Externí odkaz:
https://doaj.org/article/db5713c2a5c8453597a1beeb309a8cce
Autor:
Célestin C. Kokonendji, Sobom M. Somé
Publikováno v:
Stats, Vol 4, Iss 1, Pp 162-183 (2021)
Multivariate nonnegative orthant data are real vectors bounded to the left by the null vector, and they can be continuous, discrete or mixed. We first review the recent relative variability indexes for multivariate nonnegative continuous and count di
Externí odkaz:
https://doaj.org/article/3f978ec228374cd19691e027675a9f82