Distributional regression for data analysis
N Klein - Annual Review of Statistics and Its Application, 2024 - annualreviews.org
Flexible modeling of how an entire distribution changes with covariates is an important yet
challenging generalization of mean-based regression that has seen growing interest over …
challenging generalization of mean-based regression that has seen growing interest over …
Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting
EB Wijayanti, DRIM Setiadi… - Journal of Computing …, 2024 - dl.futuretechsci.org
Rice plays a vital role as the main food source for almost half of the global population,
contributing more than 21% of the total calories humans need. Production predictions are …
contributing more than 21% of the total calories humans need. Production predictions are …
A modeler's guide to extreme value software
This review paper surveys recent development in software implementations for extreme
value analyses since the publication of Stephenson and Gilleland (Extremes 8: 87–109,) …
value analyses since the publication of Stephenson and Gilleland (Extremes 8: 87–109,) …
Neural networks for extreme quantile regression with an application to forecasting of flood risk
The Supplementary Material contains additional information on Algorithms 1 and 2, the
simulation study on independent data, additional results for the simulation study on …
simulation study on independent data, additional results for the simulation study on …
Efficient data transmission on wireless communication through a privacy-enhanced blockchain process
In the medical era, wearables often manage and find the specific data points to check
important data like resting heart rate, ECG voltage, SPO2, sleep patterns like length …
important data like resting heart rate, ECG voltage, SPO2, sleep patterns like length …
Modeling Viscosity of CO2–N2 Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM
H Zheng, A Mahmoudzadeh, B Amiri-Ramsheh… - ACS …, 2023 - ACS Publications
Carbon dioxide (CO2) has an essential role in most enhanced oil recovery (EOR) methods
in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an …
in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an …
Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems
Climate warming is expected to increase global methane (CH4) emissions from wetland
ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can …
ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can …
Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Y Boulaguiem, J Zscheischler, E Vignotto… - Environmental Data …, 2022 - cambridge.org
Modeling dependencies between climate extremes is important for climate risk assessment,
for instance when allocating emergency management funds. In statistics, multivariate …
for instance when allocating emergency management funds. In statistics, multivariate …
Extremal random forests
Classical methods for quantile regression fail in cases where the quantile of interest is
extreme and only few or no training data points exceed it. Asymptotic results from extreme …
extreme and only few or no training data points exceed it. Asymptotic results from extreme …
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
The forecast accuracy of machine learning (ML) weather prediction models is improving
rapidly, leading many to speak of a “second revolution in weather forecasting.” With …
rapidly, leading many to speak of a “second revolution in weather forecasting.” With …