Content
and Copyright
i
Superseded
This Document Has Been Replaced By:
i
Retired
This Document Has Been Retired
i
Up-to-date Information
This is the latest update:
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing.
-
Published Date:
August 06 2018
-
Source:Biometrics. 75(1):193-201
-
Language:English
-
[PDF-770.83 KB]
Details:
-
Alternative Title:Biometrics
-
Personal Author:
-
Description:Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective and well suited for characterizing a surface in two or three dimensions, but they may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose-response curves are cross sections of a surface defined by a chemical's structural properties, a model is developed to characterize this surface to predict untested chemicals' dose-responses. This manuscript proposes a novel approach that models the multidimensional surface as a sum of learned basis functions formed as the tensor product of lower dimensional functions, which are themselves representable by a basis expansion learned from the data. The model is described and a Gibbs sampling algorithm is proposed. The approach is investigated in a simulation study and through data taken from the US EPA's ToxCast high throughput toxicity testing platform.
-
Subject:
-
Pubmed ID:30081432
-
Pubmed Central ID:PMC6363906
-
Document Type:
-
Collection(s):
-
Main Document Checksum:
- File Type:
-
Supporting Files:
text/xml 
application/pdf 
image/gif 
image/jpeg 
image/gif 
image/jpeg 
image/gif 
image/jpeg 
image/gif 
image/jpeg
No Related Documents.
Email
CDC-INFO
