From 501add7aec9b2456238039cb0ca27bdf73353fbe Mon Sep 17 00:00:00 2001 From: Tennessee Leeuwenburg Date: Sun, 10 Sep 2023 14:11:04 +1000 Subject: [PATCH] Paper updates --- docs/paper.bib | 10 ++++++++++ docs/paper.md | 4 ++-- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/docs/paper.bib b/docs/paper.bib index 538f0526..da85d1b8 100644 --- a/docs/paper.bib +++ b/docs/paper.bib @@ -151,4 +151,14 @@ @Article{Taggart:2022c pages = {201--231}, year = {2022}, publisher = {The Institute of Mathematical Statistics and the Bernoulli Society} +} +@Article{Gneiting:2011, + title = {Comparing density forecasts using threshold-and quantile-weighted scoring rules}, + author = {Gneiting, Tilmann and Ranjan, Roopesh}, + journal = {Journal of Business & Economic Statistics}, + volume = {29}, + number = {3}, + pages = {411--422}, + year = {2011}, + publisher = {Taylor & Francis} } \ No newline at end of file diff --git a/docs/paper.md b/docs/paper.md index 87fcd983..d969ce81 100644 --- a/docs/paper.md +++ b/docs/paper.md @@ -24,11 +24,11 @@ bibliography: paper.bib # Summary -`scores` is a Python package containing mathematical functions for the verification, evaluation, and optimisation of model outputs and predictions. It primarily supports the geoscience and earth system science communities. `scores` is focused on supporting xarray datatypes for earth system data. It has wide potential application in machine learning, and domains other than meteorology, geoscience and weather. It also aims to be compatible with pandas, geopandas, pangeo and work with NetCDF4, Zarr, hdf5 and GRIB data sources among others. Scores is designed to utilise Dask for scaling and performance. +`scores` is a Python package containing mathematical functions for the verification, evaluation, and optimisation of model outputs and predictions. It primarily supports the geoscience and earth system science communities. `scores` is focused on supporting xarray datatypes for earth system data. It has wide potential application in machine learning, and domains other than meteorology, geoscience and weather. It also aims to be compatible with xarray, pandas, geopandas and work with NetCDF4, Zarr, hdf5 and GRIB data sources among others. Scores is designed to utilise Dask for scaling and performance. All of the scores and metrics in this package have undergone a thorough statistical and scientific review. Every score has a companion Jupyter Notebook demonstrating its use in practise. -At the time of writing, the scores contained in this package are: MSE, MAE, RMSE, FIRM [@Taggart:2022a], CRPS, the FlipFlop index [@Griffiths:2019] and the Murphy score [@Ehm:2016]. It also includes the Diebold-Mariano statistical test [@Diebold:1995] with both the [@Harvey:1997] and [@Hering:2011] modifications. +At the time of writing, the scores contained in this package are: MSE, MAE, RMSE, FIRM [@Taggart:2022a], CRPS (including threshold-weighting, see [@Gneiting:2011], the FlipFlop index [@Griffiths:2019] and the Murphy score [@Ehm:2016]. It also includes the Diebold-Mariano statistical test [@Diebold:1995] with both the [@Harvey:1997] and [@Hering:2011] modifications. # Statement of need