On model selection criteria in multimodel analysis

Model Selection and Multimodel Inference

Analysis of different model selection criteria

In this report we analyze different model selection criteria that were used in statistics, reconstruction, recognition and other areas. We concentrate on criteria based on Bayesian theory, MDL theory, and predicting future residuals. Our analysis showed that these criteria often are not consistent when the number of points in the underlying data of the model is changed or if the scale of the da...

An investigation of model selection criteria for technical analysis of moving average

Moving averages are one of the most popular and easy-to-use tools available to a technical analyst, and they also form the building blocks for many other technical indicators and overlays. Building a moving average (MA) model needs determining four factors of (1) approach of issuing signals, (2) technique of calculating MA, (3) length of MA, and (4) band. After a literature re...

Nonnested Model Selection Criteria

This paper studies model selection for a general class of models based on minimizing random distance functions. The proposed model selection criteria are consistent, regardless of whether models are nested or nonnested and regardless of whether models are correctly specified or not, in the sense that they select the best model with the least number of parameters with probability converging to 1...

Consistent Model Selection Criteria on High Dimensions

Asymptotic properties of model selection criteria for high-dimensional regression models are studied where the dimension of covariates is much larger than the sample size. Several sufficient conditions for model selection consistency are provided. Non-Gaussian error distributions are considered and it is shown that the maximal number of covariates for model selection consistency depends on the ...