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sinorder

于 2013-12-05 发布 文件大小:1KB
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  为sinudoidal模型的AIC阶估计.AIC信息准则即Akaike information criterion,是衡量统计模型拟合优良性的一种标准,又由于它为日本统计学家赤池弘次创立和发展的,因此又称赤池信息量准则。它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。 在一般的情况下,AIC可以表示为: AIC=2k-2ln(L) 其中:k是参数的数量,L是似然函数。 假设条件是模型的误差服从独立正态分布。 让n为观察数,RSS为剩余平方和,那么AIC变为: AIC=2k+nln(RSS/n) 增加自由参数的数目提高了拟合的优良性,AIC鼓励数据拟合的优良性但是尽量避免出现过度拟合(Overfitting)的情况。所以优先考虑的模型应是AIC值最小的那一个。赤池信息准则的方法是寻找可以最好地解释数据但包含最少自由参数的模型。(AIC order estimation for sinudoidal model)

文件列表:

sinorder.m,1277,2003-10-10

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