Model Comparison

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# Model Comparison ## 10.1 Nested Model Tests **Full vs. Reduced model:** $F = \frac{(SS_{res,R} - SS_{res,F}) / (df_R - df_F)}{SS_{res,F} / df_F}$ ## 10.2 Likelihood Ratio Test $LR = -2[\ln(L(\hat{\theta}_0)) - \ln(L(\hat{\theta}_1))] \sim \chi^2_{df}$ ## 10.3 Information Criteria - AIC = $-2\ln(L) + 2k$ (minimize)...

Model Comparison

10.1 Nested Model Tests

Full vs. Reduced model:

F = \frac{(SS_{res,R} - SS_{res,F}) / (df_R - df_F)}{SS_{res,F} / df_F}

10.2 Likelihood Ratio Test

LR = -2[\ln(L(\hat{\theta}_0)) - \ln(L(\hat{\theta}_1))] \sim \chi^2_{df}

10.3 Information Criteria

AIC = $-2\ln(L) + 2k$ (minimize)
BIC = $-2\ln(L) + k\ln(n)$ (minimize, penalizes complexity more) Join Discord PreviousMixed Effects ModelsNextRegularization