penalized包执行lasso (L1) 和ridge (L2)惩罚回归模型(penalized regression models)(http://cran.r-project.org/web/packages/penalized/index.html)。pamr包执行缩小重心分类法(shrunken centroids classifier)(http://cran.r-project.org/web/packages/pamr/index.html)。earth包可做多元自适应样条回归(multivariate...
Clusterwise linear regressionpenalized likelihoodregularized MLcovariate selectionIn clusterwise regression analysis, the goal is to predict a response variable based on a set of explanatory variables, each with cluster-specific effects. In many real-life problems, the number of candidate predictors is ...
from sklearn.linear_model import Lasso,LassoCV #通过交叉验证求出最优的参数 Lambdas=np.logspace(...
LASSO是纯算法的解法，他是LAR（least angle regression）的升级版本，解法也一样，就是一个iteration的...
6、请教 lasso regression 和bridge logistic regression 你可以去看一下网址“http://www-stat.stanford.edu/~tibs/lasso.html”上下载文章“Penalized regressions: the bridge vs the lasso”看一下就知道了,如果想了了解这方面更详细的信息,可加qq:381823441,他的硕士论文做的就是这方面的内容。
LASSO-penalized clusterwise linear regression modelling: a two-step approach In clusterwise regression analysis, the goal is to predict a response variable based on a set of explanatory variables, each with cluster-specific effects... RD Mari,R Rocci,SA Gattone - 《Journal of Statistical Computation...
6、请教 lasso regression 和bridge logistic regression 你可以去看一下网址“”上下载文章“Penalized regressions: the bridge vs the lasso”看一下就知道了,如果想了了解这方面更详细的信息,可加qq:381823441,他的硕士论文做的就是这方面的内容。 7、LASSO有很多令人期待的问题没有解决,所以还是有很多坑可以去...
answers on a similar question asked here, but it didn't quite answer my question. There's an excellent tutorial on the R package that I'm using here, and the author Jelle Goeman had the following note at the end of the tutorial regarding confidence intervals from penalized ...
使用BIC 选择Lasso惩罚参数。作为一种“惩罚回归”(penalized regression),在进行Lasso估计时,需要选择惩罚参数(penalty parameter)。在Stata 16中,可使用交叉验证(cross-validation)、适应性方法(adaptive method)或代入法(plugin)来选择惩罚参数。 在Stata 17中,新增了选择项 “selection(bic)”,可使用 “贝叶斯信息准...
6、请教lassoregression和bridgelogisticregression 你可以去看一下网址“http://www-stat.stanford.edu/~tibs/lasso.html”上下载文章“Penalizedregressions:thebridgevsthelasso”看一下就知道了，如果想了了解这方面更详细的信息，可加qq：381823441，他的硕士论文做的就是这方面的内容。 7、LASSO有很多令人期待的...