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Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis , this may be the selection of a statistical model from a set of candidate models, given data.
Van der Pas and Grünwald prove that model selection based on a modified Bayesian estimator, the so-called switch distribution, in many cases behaves asymptotically like HQC, while retaining the advantages of Bayesian methods such as the use of priors etc.
The clearest case is where precision is taken to be mean squared error, say = + in terms of squared bias and variance for the estimator associated with model . FIC formulae are then available in a variety of situations, both for handling parametric , semiparametric and nonparametric situations, involving separate estimation of squared bias and ...
This fragility came to motivate the work of Edward Leamer, who emphatically criticized modelers' tendency to indulge in "post-data model construction" and consequently developed a method of economic modelling based on the selection of regression models according to the types of prior density specification in order to identify the prior ...
A basic tool for econometrics is the multiple linear regression model. [8] Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods. [ 9 ] [ 10 ] Econometricians try to find estimators that have desirable statistical properties including unbiasedness , efficiency , and consistency .
In efficient quantile regression, an EL-based categorization [9] procedure helps determine the shape of the true discrete distribution at level p, and also provides a way of formulating a consistent estimator. In addition, EL can be used in place of parametric likelihood to form model selection criteria. [10]
A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process . [ 1 ]
In general, if the goal is prediction, AIC and leave-one-out cross-validations are preferred. If the goal is selection, inference, or interpretation, BIC or leave-many-out cross-validations are preferred. A comprehensive overview of AIC and other popular model selection methods is given by Ding et al. (2018) [30]