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  2. Akaike information criterion - Wikipedia

    en.wikipedia.org/wiki/Akaike_information_criterion

    The t-test assumes that the two populations have identical standard deviations; the test tends to be unreliable if the assumption is false and the sizes of the two samples are very different (Welch's t-test would be better). Comparing the means of the populations via AIC, as in the example above, has an advantage by not making such assumptions.

  3. Deviance information criterion - Wikipedia

    en.wikipedia.org/wiki/Deviance_information_criterion

    The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo (MCMC) simulation. DIC is an asymptotic approximation as the ...

  4. Bayesian information criterion - Wikipedia

    en.wikipedia.org/wiki/Bayesian_information_criterion

    v. t. e. In statistics, the Bayesian information criterion (BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among a finite set of models; models with lower BIC are generally preferred. It is based, in part, on the likelihood function and it is closely related to the Akaike information criterion (AIC).

  5. Hannan–Quinn information criterion - Wikipedia

    en.wikipedia.org/wiki/Hannan–Quinn_information...

    In statistics, the Hannan–Quinn information criterion (HQC) is a criterion for model selection. It is an alternative to Akaike information criterion (AIC) and Bayesian information criterion (BIC). It is given as. where is the log-likelihood, k is the number of parameters, and n is the number of observations.

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  7. Likelihood-ratio test - Wikipedia

    en.wikipedia.org/wiki/Likelihood-ratio_test

    Likelihood-ratio test. In statistics, the likelihood-ratio test is a hypothesis test that involves comparing the goodness of fit of two competing statistical models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods.

  8. Deviance (statistics) - Wikipedia

    en.wikipedia.org/wiki/Deviance_(statistics)

    Deviance (statistics) In statistics, deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing. It is a generalization of the idea of using the sum of squares of residuals (SSR) in ordinary least squares to cases where model-fitting is achieved by maximum likelihood.

  9. List of analyses of categorical data - Wikipedia

    en.wikipedia.org/wiki/List_of_analyses_of...

    Coefficient of colligation - Yule's Y. Coefficient of consistency. Coefficient of raw agreement. Conger's Kappa. Contingency coefficient – Pearson's C. Cramér's V. Dice's coefficient. Fleiss' kappa. Goodman and Kruskal's lambda.