赤池資訊量準則，即Akaike information criterion、簡稱AIC，是衡量統計模型擬合優良性的一種標準，是由日本統計學家赤池弘次創立和發展的。赤池資訊量準則建立在熵的概念基礎上，可以權衡所估計模型的複雜度和此模型擬合數據的優良性。
優先考慮的模型應是AIC值最小的那一個。

貝葉斯資訊準則，BIC＝ Bayesian Information Criterions

The log likelihood of the model is the value that is maximized by the process that computes the maximum likelihood value for the Bi parameters. 

The Deviance is equal to -2*log-likelihood.

Akaike’s Information Criterion (AIC) is -2*log-likelihood+2*k where k is the number of estimated parameters. 

The Bayesian Information Criterion (BIC) is -2*log-likelihood + k*log(n) where k is the number of estimated parameters and n is the sample size.  The Bayesian Information Criterion is also known as the Schwartz criterion

DTREG和最新版的SPSS都可以直接給出