Schwarz criterion or Bayesian information criterion (BIC) [BIC]

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“A goodness of fit measure of forecast error based on the squared difference between observed and predicted Y values. It is used for model selection. BIC = -2 ln(L) + k ln(n) where k is the number of estimated parameters, n is the number of observations and L is the likelihood function. Usually simplified to BIC=residual sum of squares/error variance + k ln(n). Also see Akaike information criteria”

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By |February 1st, 2019|Comments Off on Schwarz criterion or Bayesian information criterion (BIC) [BIC]