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Bayesian Information Criterion (BIC)

The equation for computing the BIC for a k-joinpoint model is:

BIC(k) = ln{SSE(k)/#Obs} + {#Parm(k) /#Obs} * ln(#Obs),

where SSE is the sum of squared errors of the k-joinpoint regression model, #Parm(k)=2k+2 is the number of parameters of the k-joinpoint model and #Obs is the number of observations.

The k-joinpoint model with the minimum value of BIC(k) is selected as the final model.