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  2. Akaike information criterion - Wikipedia

    en.wikipedia.org/wiki/Akaike_information_criterion

    More generally, we might want to compare a model of the data with a model of transformed data. Following is an illustration of how to deal with data transforms (adapted from Burnham & Anderson (2002, §2.11.3): "Investigators should be sure that all hypotheses are modeled using the same response variable").

  3. Hirotugu Akaike - Wikipedia

    en.wikipedia.org/wiki/Hirotugu_Akaike

    The Akaike information criterion (AIC) is an estimator of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models.

  4. Goodness of fit - Wikipedia

    en.wikipedia.org/wiki/Goodness_of_fit

    In assessing whether a given distribution is suited to a data-set, the following tests and their underlying measures of fit can be used: Bayesian information criterion; Kolmogorov–Smirnov test; Cramér–von Mises criterion; Anderson–Darling test; Berk-Jones tests [1] [2] Shapiro–Wilk test; Chi-squared test; Akaike information criterion ...

  5. Watanabe–Akaike information criterion - Wikipedia

    en.wikipedia.org/wiki/Watanabe–Akaike...

    In statistics, the Widely Applicable Information Criterion (WAIC), also known as Watanabe–Akaike information criterion, is the generalized version of the Akaike information criterion (AIC) onto singular statistical models. [1] It is used as measure how well will model predict data it wasn't trained on.

  6. BCBS 239 - Wikipedia

    en.wikipedia.org/wiki/BCBS_239

    BCBS 239 is the Basel Committee on Banking Supervision's standard number 239. The subject title of the standard is: "Principles for effective risk data aggregation and risk reporting".

  7. A New Study Pinpointed Exactly How To Calculate Your ...

    www.aol.com/study-pinpointed-exactly-calculate...

    Basically, using oral swabs and stacking it against data from blood tissue is like trying to compare apples to oranges. “Because most clocks have been constructed using blood tissue, it follows ...

  8. 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.

  9. Australia's Macquarie to invest up to $5 billion in Applied ...

    www.aol.com/news/macquarie-invest-5-billion...

    Macquarie's asset management arm has agreed to invest up to $900 million in a data center campus that Applied Digital is developing in North Dakota. Dallas, Texas-based Applied Digital also has ...