Model Selection Using Information Theory and the MDL Principle  

Oleh:

Stine, Robert A.

Jenis: Article from Bulletin/Magazine
Dalam koleksi: Sociological Methods and Research vol. 33 no. 02 (Nov. 2004), page 230.
Topik: Akaike Information Criterion (AIC); Bayes Information Criterion (BIC); Risk Inflation Criterion (RIC); Cross-Validation; Model Selection; Stepwise Regression; Regression tree

Isi artikel

Information theory offers s coherent, intuitive view of model selection. this perspective arises from thinking of a statistical model as a code, an algorithm for compressing data into a sequence of bits. the description length is the length of this code for the data plus the length of a descriptions of the models isself. the length of the code for the data measures the fit of the model to the data, wheres the length of the code for the model measures its complexity. The minimum description length (MDL) principle picks the model with smalless description length, balancing fit versus complexity. variations on MDL reproduct other well-known methods of models selection. Going further, information theory allows one to choose from among various types of models, permitting the comparison of tree-based models to regressions. A running example compares several models for the wll-known Boston housing data.

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