Torrent details for "Bellavia A. Statistical Methods for Environmental Mixtures. A Pr…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
5.2 MB
Info Hash:
A4FE41A533A61CA246DBA1A47AB89E9348C6C168
Added By:
Added:
Sept. 23, 2025, 11:27 p.m.
Stats:
|
(Last updated: Sept. 23, 2025, 11:30 p.m.)
| File | Size |
|---|---|
| Bellavia A. Statistical Methods for Environmental Mixtures. A Primer...2025.pdf | 5.2 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Bellavia A. Statistical Methods for Environmental Mixtures. A Primer...2025
-----------------------------------------------------------------------------------
COVER

-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format This book provides a comprehensive introduction to statistical approaches for the assessment of complex environmental exposures, such as pollutants and chemical mixtures, within the exposome framework. Environmental mixtures are defined as groups of 3 or more chemical/pollutants, simultaneously present in nature, consumer products, or in the human body. Assessing the health effects of environmental mixtures poses several methodological challenges due to the high levels of correlation that are often present between environmental chemicals, and by the need of incorporating flexible non-additive and non-linear effects that can capture and describe the complex mechanisms by which environmental exposure contribute to diseases. Several statistical approaches are proposed and discussed, including the application of regression-based approaches (e.g. penalized regression such as LASSO and elastic net, or Bayesian variable selection) for environmental exposures, and novel methods (e.g. weighted quantile sum regression, or Bayesian Kernel Machine Regression) that account for specific complexities of environmental exposures. More recent efforts included are the application of machine learning approaches (e.g. gradient boosting) for environmental data
×


