Scientific Research Publishing

Comparative Performance of Propensity Score Methods for Clinical Multi-Group Data: Balancing Confounders and Estimating Treatment Effects ()

1 Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China. 2 School of International Education, Southern Medical University, Guangzhou, China. 3 Guangdong ...
GitHub

Generalized Additive Models in Python.

🚀 Version 0.12.0 out now! See release notes here. pyGAM is a package for building Generalized Additive Models in Python, with an emphasis on modularity and performance. The API is designed for users ...
IEEE

Theoretical Foundations of the Echo Envelope Statistical Modeling: A Tutorial

Abstract: The purpose of this methods and concepts tutorial is to present the homodyned K-distribution (HKD) statistical modeling of the echo envelope of received radio frequency (RF) signals in the ...
Frontiers

Toward interpretable expected goals modeling using Bayesian mixed models

Empowered by technological progress, sports teams and bookmakers strive to understand relationships between player and team activity and match outcomes. For this purpose, the probability of an event ...
PNAS

An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies

Causal inference has been increasingly essential in modern observational studies with rich covariate information. However, it is often challenging to estimate the causal effect with high-dimensional ...
The Lancet

Non-optimal temperature-attributable mortality and morbidity burden by cause, age and sex under climate and population change scenarios: a nationwide modelling study in Japan

aDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan bSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan ...
Europe PMC

A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents.

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Europe PMC

Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification.

Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve ...