Results 211 to 220 of about 44,356 (305)
Investigation of Combined Toxic Metals, PFAS, Volatile Organic Compounds, and Essential Elements in Chronic Kidney Disease. [PDF]
Adetunji AG, Obeng-Gyasi E.
europepmc +1 more source
Improved Centile Estimation by Transformation And/Or Adaptive Smoothing of the Explanatory Variable
ABSTRACT A popular approach to growth reference centile estimation is the LMS (Lambda‐Mu‐Sigma) method, which assumes a parametric distribution for response variable Y$$ Y $$ and fits the location, scale and shape parameters of the distribution of Y$$ Y $$ as smooth functions of explanatory variable X$$ X $$.
R. A. Rigby +2 more
wiley +1 more source
Quantile Regression in Epidemiology: Capturing Heterogeneity Beyond the Mean. [PDF]
Gnardellis C.
europepmc +1 more source
Seamless Short‐ to Mid‐Term Probabilistic Wind Power Forecasting
ABSTRACT This paper brings a new understanding to the relative importance of different uncertainty sources across forecast horizons up to 7 days ahead. It presents a method for probabilistic wind power forecasting that quantifies uncertainty from weather forecasts and weather‐to‐power conversion separately.
Gabriel Dantas, Jethro Browell
wiley +1 more source
A Regionally Determined Climate‐Informed West Nile Virus Forecast Technique
Abstract West Nile virus (WNV) infection has caused over 30,000 human cases of the severe, neuroinvasive form of the disease (West Nile virus Neuroinvasive Disease; WNND) and nearly 3,000 deaths in the U.S. Despite known links to observable climate factors, no effective nationwide WNV or WNND forecast exists.
Ryan D. Harp +5 more
wiley +1 more source
Statistical analysis plan for the Australasian Resuscitation in sepsis evaluation: FLUid or vasopressors in emergency department sepsis (ARISE FLUIDS) trial. [PDF]
Milford EM +6 more
europepmc +1 more source
A Causal‐Spatial Explainability Framework for Geological Hazard Susceptibility Modeling
Abstract Data‐driven susceptibility models often act as black boxes, predicting where hazards may occur without explaining why. To address this gap, we propose a Causal‐Prior (CP) framework that integrates validated causal knowledge into training while yielding spatially grounded explanations.
Bo‐Fan Yu +6 more
wiley +1 more source

