article · 2000

icomp: Informational complexity measures

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Abstract

This document provides formulae (from [3]) on which the calculations by icomp are based. icomp calculates Akaike information criteria (AIC), Schwartz Bayesian information criteria (SBC, SBIC), and Bozdogan’s index of informational complexity (ICOMP). These criteria are used to select the “best” model compromising an adequate goodness of fit and a small number of parameters by adding a penalty for overparametrization to the lack of fit measure (estimate of the maximum likelihood or the residual sum of squares). The best model minimizes the criterion. The three informational measures differ in the penalty term, SBIC penalizing more severely for larger samples, and ICOMP accounting for covariance structure of a model (and, thus, for collinearity between the factors and dependence among the parameter estimates).

Cite this paper

APA 7

Kolenikov, S. (2000). icomp: Informational complexity measures.

MLA 9

Kolenikov, Stanislav. “icomp: Informational complexity measures.” 2000.

Chicago

Kolenikov, Stanislav. 2000. “icomp: Informational complexity measures.”

Harvard

Kolenikov, S. (2000) ‘icomp: Informational complexity measures’.

BibTeX

@article{kolenikov2000icomp,
  title = {{icomp: Informational complexity measures}},
  author = {Kolenikov, Stanislav},
  year = {2000}
}

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