In this chapter, we discuss using Kullback–Leibler information as a criterion for evaluating statistical models that approximate the true probability distribution of the data and its properties. We also explain how this criterion for evaluating statistical models leads to the concept of the information criterion, AIC. To this end, we explain the basic framework of model evaluation and the derivation of AIC by adopting a unified approach.
KeywordsAkaike Information Criterion Maximum Likelihood Estimator Variance Covariance Matrix True Order True Distribution
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