MODEL SELECTION METHOD BASED ON MAXIMAL INFORMATION COEFFICIENT OF RESIDUALS

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this ar- ticle, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coe~cient （MIC）, a recently proposed dependence measure, captures a wide range of associ- ations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient （PMIC） is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.     

基于EMD及非平稳性度量的趋势噪声分解方法

该文结合非平稳性度量,研究利用经验模态分解算法进行趋势噪声分解,提出基于非平稳性度量的准则来判定舍弃的本性模态函数的数目.通过数值模拟证明了该准则克服了连续均方误差准则的缺陷,在不同噪声强度和复杂趋势下,都能够达到很好的去噪效果.     

基于非平稳性度量的彩票数据实证分析

非平稳性度量是衡量时间序列平稳程度的方法.利用非平稳度量,给出了C检验,并结合非平稳性度量值,对我国体彩"排列五"、"七星彩"及美国亚利桑那州的博彩"Pick3"的历史数据进行分析,发现博彩各数位上整数"0~9"出现都拥有稳定的概率,但并不是以等概率1/10出现,其分布与i.i.d均匀分布稍有差异,其中"七星彩"均匀性最好,"Pick3"的均匀性次之,"排列五"均匀性稍差.