An Introduction to Model Selection.

@article{Zucchini2000AnIT,
  title={An Introduction to Model Selection.},
  author={Zucchini},
  journal={Journal of mathematical psychology},
  year={2000},
  volume={44 1},
  pages={
          41-61
        }
}
  • Zucchini
  • Published 1 March 2000
  • Economics
  • Journal of mathematical psychology
This paper is an introduction to model selection intended for nonspecialists who have knowledge of the statistical concepts covered in a typical first (occasionally second) statistics course. [] Key Method The ideas are illustrated using an example in which observations are available for the entire population of interest. This enables us to examine and to measure effects that are usually invisible, because in practical applications only a sample from the population is observed. The problem of selection bias…

Figures and Tables from this paper

A Focused Bayesian Information Criterion
Myriads of model selection criteria (Bayesian and frequentist) have been proposed in the literature aiming at selecting a single model regardless of its intended use. An honorable exception in the
Key Concepts in Model Selection: Performance and Generalizability.
  • E M Forster
  • Biology
    Journal of mathematical psychology
  • 2000
TLDR
It seems that simplicity and parsimony may be an additional factor in managing these errors, in which case the standard methods of model selection are incomplete implementations of Occam's razor.
Estimating and Correcting the Effects of Model Selection Uncertainty
Most applied statistical analyses are carried out under model uncertainty, meaning that the model which generated the observations is unknown, and so the data are first used to select one of a set of
Estimation of a multivariate mean under model selection uncertainty
Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with
Effects of Bayesian Model Selection on Frequentist Performances: An Alternative Approach
It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance; i.e. ignoring model selection uncertainty. The resulted estimator is called
Statistical Tests for Comparing Possibly Misspecified and Nonnested Models.
  • Golden
  • Mathematics
    Journal of mathematical psychology
  • 2000
TLDR
A large sample Model Selection Test (MST), is introduced that can be used in conjunction with most existing MSC procedures to decide if the estimated goodness-of-fit for one model is significantly different from the estimated goodies for another model.
Post-model selection inference and model averaging
Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by
Model Selection in Regression: Application to Tumours
The problem of model selection is at the core of progress in science. Over the decades, scientists have used various statistical tools to select among alternative models of data. A common challenge
...
...

References

SHOWING 1-10 OF 22 REFERENCES
Bayesian Model Selection in Social Research
It is argued that P-values and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent
Model selection: An integral part of inference
We 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.
Model uncertainty, data mining and statistical inference
TLDR
The effects of model uncertainty, such as too narrow prediction intervals, and the non-trivial biases in parameter estimates which can follow data-based modelling are reviewed.
The Importance of Complexity in Model Selection.
  • Myung
  • Computer Science
    Journal of mathematical psychology
  • 2000
TLDR
It is shown that model selection based solely on the fit to observed data will result in the choice of an unnecessarily complex model that overfits the data, and thus generalizes poorly.
On Measuring and Correcting the Effects of Data Mining and Model Selection
TLDR
The concept of GDF offers a unified framework under which complex and highly irregular modeling procedures can be analyzed in the same way as classical linear models and many difficult problems can be solved easily.
Bayesian Tests and Model Diagnostics in Conditionally Independent Hierarchical Models
TLDR
This article explores how MCMC simulation algorithms and other related computational algorithms can be used to compute Bayes factors that are useful in criticizing a particular CIHM.
Regression and time series model selection in small samples
SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small,
A note on bootstrap model selection criterion
Bayesian Model Selection and Model Averaging.
  • Wasserman
  • Economics
    Journal of mathematical psychology
  • 2000
This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss
Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
Abstract The Bayes factor is a ratio of two posterior normalizing constants, which may be difficult to compute. We compare several methods of estimating Bayes factors when it is possible to simulate
...
...