赤池信息量准则，即Akaike information criterion、简称AIC，是衡量统计模型拟合优良性的一种标准，是由日本统计学家赤池弘次创立和发展的。赤池信息量准则建立在熵的概念基础上，可以权衡所估计模型的复杂度和此模型拟合数据的优良性。
优先考虑的模型应是AIC值最小的那一个。
贝叶斯信息准则，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都可以直接给出
赤池信息量准则，即Akaike information criterion、简称AIC，是衡量统计模型拟合优良性的一种标准，是由日本统计学家赤池弘次创立和发展的。赤池信息量准则建立在熵的概念基础上，可以权衡所估计模型的复杂度和此模型拟合数据的优良性。
优先考虑的模型应是AIC值最小的那一个。
贝叶斯信息准则，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都可以直接给出