scholarly journals Model Selection and Multimodel Inference

2004 ◽  
Keyword(s):  
Model Selection ◽  
Multimodel Inference

A multimodel inference approach to categorical variant choice: construction, priming and frequency effects on the choice between full and contracted forms of am, are and is

2017 ◽  
Vol 13 (2) ◽  
pp. 203-260 ◽  
Author(s):  
Danielle Barth ◽  
Vsevolod Kapatsinski
Keyword(s):  
Model Selection ◽  
Prior Work ◽  
Single Model ◽  
Frequency Effects ◽  
Multimodel Inference ◽  
Classic Problem ◽  
Model Selection Uncertainty ◽  
Corpus Data ◽  
Selection For ◽  
Auxiliary Contraction

AbstractThe present paper presents a multimodel inference approach to linguistic variation, expanding on prior work by Kuperman and Bresnan (2012). We argue that corpus data often present the analyst with high model selection uncertainty. This uncertainty is inevitable given that language is highly redundant: every feature is predictable from multiple other features. However, uncertainty involved in model selection is ignored by the standard method of selecting the single best model and inferring the effects of the predictors under the assumption that the best model is true. Multimodel inference avoids committing to a single model. Rather, we make predictions based on the entire set of plausible models, with contributions of models weighted by the models' predictive value. We argue that multimodel inference is superior to model selection for both the I-Language goal of inferring the mental grammars that generated the corpus, and the E-Language goal of predicting characteristics of future speech samples from the community represented by the corpus. Applying multimodel inference to the classic problem of English auxiliary contraction, we show that the choice between multimodel inference and model selection matters in practice: the best model may contain predictors that are not significant when the full set of plausible models is considered, and may omit predictors that are significant considering the full set of models. We also contribute to the study of English auxiliary contraction. We document the effects of priming, contextual predictability, and specific syntactic constructions and provide evidence against effects of phonological context.

Model selection and multimodel inference for standardizing catch rates of bycatch species: a case study of oceanic whitetip shark in the Hawaii-based longline fishery

2013 ◽  
Vol 70 (12) ◽  
pp. 1723-1740 ◽  
Author(s):  
Jon Brodziak ◽  
William A. Walsh
Keyword(s):  
Model Selection ◽  
Negative Binomial ◽  
Longline Fishery ◽  
Catch Per Unit Effort ◽  
Multimodel Inference ◽  
Stepwise Variable Selection ◽  
Alternative Hypotheses ◽  
Pelagic Longline Fishery ◽  
Catch Rates

One key issue for standardizing catch per unit effort (CPUE) of bycatch species is how to model observations of zero catch per fishing operation. Typically, the fraction of zero catches is high, and catch counts may be overdispersed. In this study, we develop a model selection and multimodel inference approach to standardize CPUE in a case study of oceanic whitetip shark (Carcharhinus longimanus) bycatch in the Hawaii-based pelagic longline fishery. Alternative hypotheses for shark catch per longline set were characterized by the variance to mean ratio of the count distribution. Zero-inflated and non-inflated Poisson, negative binomial, and delta-gamma models were fit to fishery observer data using stepwise variable selection. Alternative hypotheses were compared using multimodel inference. Results from the best-fitting zero-inflated negative binomial model showed that standardized CPUE of oceanic whitetip sharks decreased by about 90% during 1995–2010 because of increased zero catch sets and decreased CPUE on sets with positive catch. Our model selection approach provides an objective way to address the question of how to treat zero catches when analyzing bycatch CPUE.

Model Selection and Multimodel Inference

2015 ◽  
pp. 175-196 ◽  
Author(s):  
Fränzi Korner-Nievergelt ◽  
Tobias Roth ◽  
Stefanie von Felten ◽  
Jérôme Guélat ◽  
Bettina Almasi ◽  
...  
Keyword(s):  
Model Selection ◽  
Multimodel Inference
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