scholarly journals The Effect of Sample Composition on Inference for Random Effects Using Normal and Dirichlet Process Models

2021 ◽  
Vol 8 (4) ◽  
pp. 579-595
Author(s):  
Guofen Yan ◽  
J. Sedransk
Keyword(s):  
Random Effects ◽  
Dirichlet Process ◽  
Process Models ◽  
Sample Composition

Degradation modeling based on gamma process models with random effects

2017 ◽  
pp. 0-0 ◽  
Author(s):  
Luis Alberto Rodríguez-Picón ◽  
Anna Patricia Rodríguez-Picón ◽  
Luis Carlos Méndez-González ◽  
Manuel I. Rodríguez-Borbón ◽  
Alejandro Alvarado-Iniesta
Keyword(s):  
Random Effects ◽  
Process Models ◽  
Gamma Process ◽  
Degradation Modeling

scholarly journals Modeling Joint Abundance of Multiple Species Using Dirichlet Process Random Effects

2016 ◽  
Author(s):  
Devin S. Johnson ◽  
Elizabeth H. Sinclair
Keyword(s):  
Community Structure ◽  
Random Effects ◽  
Bering Sea ◽  
Dirichlet Process ◽  
Mesopelagic Fish ◽  
Ecological Groups ◽  
Eastern Bering Sea ◽  
Cross Correlations ◽  
Multiple Species ◽  
Occurrence Of Species

SummaryWe present a method for modeling multiple species distributions simultaneously using Dirichlet Process random effects to cluster species into guilds. Guilds are ecological groups of species that behave or react similarly to some environmental conditions. By modeling latent guild structure, we capture the cross-correlations in abundance or occurrence of species over surveys. In addition, ecological information about the community structure is obtained as a byproduct of the model. By clustering species into similar functional groups, prediction uncertainty of community structure at additional sites is reduced over treating each species separately. The method is illustrated with a small simulation demonstration, as well as an analysis of a mesopelagic fish survey from the eastern Bering Sea near Alaska. The simulation data analysis shows that guild membership can be extracted as the differences between groups become larger and if guild differences are small the model naturally collapses all the species into a small number of guilds which increases predictive efficiency by reducing the number of parameters to that which is supported by the data.

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