基于自标度数据的偏最小二乘（PLS）回归系数是一个重要变量的理论，云永欢等提出了一种新的变量选择策略&迭代变量子集优化（IVSO）。在这项工作中，每个子模型中产生的回归系数都被规范化以消除影响。在每一轮迭代中，将从子模型中得到的各变量的回归系数相加，以评估其重要性水平。采用加权二元矩阵抽样（WBMS）和序贯加法两步法，以竞争的方式逐步、温和地消除非信息变量，降低重要变量丢失的风险。此外，还考虑到，通过交叉验证产生的潜在变量的最佳数量将对回归系数产生很大的差异，有时这种差异甚至可以变化几个数量级。

K近邻互信息计算程序matlab代码,KraskovMI算法实现，可用于计算时间序列数据之间的相关性

由邓百川等提出的一种近红外光谱特征提取算法，A bootstrapping soft shrinkage (BOSS) approach for variable selection

RBF神经网络-非线性回归分析，matlab代码，值得学习，欢迎下载

多元统计分析的简单回归和多重回归系数估计，包含python源码和实验结果

% Compute Mutual Information and Joint entropy of two images % Takes two images and returns the mutual information and joint % entropy. For implementing this function also download function % joint_histogram.m in my file exchange. % % written by Amir Pasha Mahmoudzadeh % Wright State University % Biomedical Imaging Lab

使用R进行非线性回归分析，好书一本，不要错过。。。。。。

一本很好的关于逻辑回归分析的书 Review "...The book is a classic, extremely well written, and it includes a variety of software packages and real examples...." -- The Statistician, Vol. 51, No.2, 2002 "...an excellent book that balances many objectives well.... All statistical practitioners...can benefit from this book...Applied Logistic Regression is an ideal choice." -- Technometrics, February 2002 "...it remains an extremely valuable text for everyone working or teaching in fields like epidemiology..." -- Statistics in Medicine, No.21, 2002 "...the revised text continues to provide a focused introduction to the logistic regression model and its use in methods for modelling..." -- Short Book Reviews, Vol. 21, No. 2, August 2001 "In this revised and updated edition of the popular test, the authors incorporate theoretical and computing advances from the last decade." -- Journal of the American Statistical Association, September 2001 Product Description From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."-Choice "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."-Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic reg

非线性回归分析及其应用，贝茨。值得看看，对非线性回归理论学习挺有用的。

SPSS回归分析 2015年3月1日申德拉(作者), 宋武 (译者)