Add to ORKGWe argue that model selection uncertainty should be fully incorporated into statistical inference whenever estimation is sensitive to model choice and that choice is made with reference to the data. We consider different philosophies for achieving this goal and suggest strategies for data analysis. We illustrate our methods through three examples. The first is a Poisson regression of bird counts in which a choice is to be made between inclusion of one or both of two covariates. The second is a line transect data set for which different models yield substantially different estimates of abundance. The third is a simulated example in which truth is known.</p
At first glance the goals of model selection might seem clear. Out of a set of possible models, we w...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
Variable selection methods and model selection approaches are valuable statistical tools, which are ...
The problem of statistical model selection in econometrics and statistics is reviewed. Model selecti...
Statistical inference is traditionally based on the assumption that one single model is the true mod...
Information-theoretic approaches to model selection, such as Akaike's information criterion (AIC) an...
Information-theoretic approaches to model selection, such as Akaike's information criterion (AIC) an...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
In this article, we introduce the concept of model uncertainty.We review the frequentist and Bayesia...
Picking one ‘winner’ model for researching a certain phenomenon while discarding the rest implies a ...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
At first glance the goals of model selection might seem clear. Out of a set of possible models, we w...
At first glance the goals of model selection might seem clear. Out of a set of possible models, we w...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
Variable selection methods and model selection approaches are valuable statistical tools, which are ...
The problem of statistical model selection in econometrics and statistics is reviewed. Model selecti...
Statistical inference is traditionally based on the assumption that one single model is the true mod...
Information-theoretic approaches to model selection, such as Akaike's information criterion (AIC) an...
Information-theoretic approaches to model selection, such as Akaike's information criterion (AIC) an...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
Statistical inference deals with natural variation between units and with uncertainty due to samplin...
In this article, we introduce the concept of model uncertainty.We review the frequentist and Bayesia...
Picking one ‘winner’ model for researching a certain phenomenon while discarding the rest implies a ...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
At first glance the goals of model selection might seem clear. Out of a set of possible models, we w...
At first glance the goals of model selection might seem clear. Out of a set of possible models, we w...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
Variable selection methods and model selection approaches are valuable statistical tools, which are ...