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Iterating on a single model is a viable alternative to multimodel inference
Author(s) -
Ver Hoef Jay M.,
Boveng Peter L.
Publication year - 2015
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.1002/jwmg.891
Subject(s) - inference , automatic summarization , computer science , statistical inference , process (computing) , machine learning , sampling (signal processing) , statistical model , artificial intelligence , data mining , statistics , mathematics , filter (signal processing) , computer vision , operating system
Multimodel inference accommodates uncertainty when selecting or averaging models, which seems logical and natural. However, there are costs associated with multimodel inferences, so they are not always appropriate or desirable. First, we present statistical inference in the big picture of data analysis and the deductive–inductive process of scientific discovery. Inferences on fixed states of nature, such as survey sampling methods, generally use a single model. Multimodel inferences are used primarily when modeling processes of nature, when there is no hope of knowing the true model. However, even in these cases, iterating on a single model may meet objectives without introducing additional complexity. Additionally, discovering new features in the data through model diagnostics is easier when considering a single model. There are costs for multimodel inferences, including the coding, computing, and summarization time on each model. When cost is included, a reasonable strategy may often be iterating on a single model. We recommend that researchers and managers carefully examine objectives and cost when considering multimodel inference methods. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.