日期精度：到日 5 篇 · 到月 3 篇 · 到年 0 篇
Boosting 集成的总体理论
Tree ensembles are looked at in distribution space, that is, the limit case of "infinite" sample size. It is shown that the simplest kind of trees is complete in D-dimensional $L_2(P)$ space if the number of terminal nodes T is greater than D. For such trees we show that the AdaBoost algorithm gives an ensemble converging to the Bayes risk.
Arcing 分类器：附讨论与作者答复
Recent work has shown that combining multiple versions of unstable classifiers such as trees or neural nets results in reduced test set error. One of the more effective is bagging. Here, modified training sets are formed by resampling from the original training set, classifiers constructed using these training sets and then combined by voting. Freund and Schapire propose an algorithm the basis of which is to adaptively resample and combine (hence the acronym “arcing”) so…
多元线性回归中的多响应预测
Summary We look at the problem of predicting several response variables from the same set of explanatory variables. The question is how to take advantage of correlations between the response variables to improve predictive accuracy compared with the usual procedure of doing individual regressions of each response variable on the common set of predictor variables. A new procedure is introduced called the curds and whey method. Its use can substantially reduce prediction…
模型选择中不稳定性与稳定化的启发式分析
In model selection, usually a "best" predictor is chosen from a collection ${\hat{\mu}(\cdot, s)}$ of predictors where $\hat{\mu}(\cdot, s)$ is the minimum least-squares predictor in a collection $\mathsf{U}_s$ of predictors. Here s is a complexity parameter; that is, the smaller s, the lower dimensional/smoother the models in $\mathsf{U}_s$. If $\mathsf{L}$ is the data used to derive the sequence ${\hat{\mu}(\cdot, s)}$, the procedure is called unstable if a small change in…
已知误差分布删失回归模型的简单估计程序
A simple and tractable iterative least squares estimation procedure for censored regression models with known error distributions is analyzed. It is found to be equivalent to a well-defined Huber type $M$-estimate. Under a regularity condition, the algorithm converges geometrically to a unique solution. The resulting estimate is $\sqrt N$-consistent and asymptotically normal.
Little Bootstrap 与回归维度选择：固定设计下的预测误差
When a regression problem contains many predictor variables, it is rarely wise to try to fit the data by means of a least squares regression on all of the predictor variables. Usually, a regression equation based on a few variables will be more accurate and certainly simpler. There are various methods for picking “good” subsets of variables, and programs that do such procedures are part of every widely used statistical package. The most common methods are based on stepwise…