网络释义 1. 样本外检验 ...有效的,那么基金经理接下来会对这个模型进行所谓的“样本外检验”(out-of-sample test),也就是说使用另外一组不同的历史 … blog.sina.com.cn|基于6个网页
1) Out of Sample Test 样本外检验2) verification set data 检验样本 1. A new approach, generating training set data, verification set data and testing set data and boundary set data, was put forward in this paper. 在分析现有应用人工神经网络评价模型局限性的基础上,根据湖库富营养化的评价...
样本内和样本外测试
In-sample testing is very time intensive, as the team manually checks the calculations and results. During the in-sample test, algorithms may calculate the averages and standard deviations for trades. This is the step where the team converts all the prototype examples into prototype production-...
一間公司的主管或老闆,錄取一位新人時,無論他過去履歷再輝煌,還是得經過試用期才可以將人事定案下來。在市場中交易也是一樣,策略即便有漂亮的績效,也得經過「樣本外測試」(Out-of-sample test),使用歷史績效以外的數據進行對過去測試的驗證。
Classification on data with biased class distribution - Vucetic, Obradovic - 2001 () Citation Context ...ion on out-of-sample test sets from the labeled dataset. The a priori class distribution pU can, on the other hand, be ... S Vucetic,Z Obradovic - European Conference on Machine Learni...
Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa The importance of financial instability for the wo Thompson,Kirsten,V Eyden,... - 《Emerging Markets Finance & Trade》 被引量: 4发表: 2015年 Out-of-Sample Predictability of Economic Efficiency Measures...
R Giacomini,H White - Department of Economics, UC San Diego 被引量: 2191发表: 2003年 Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis We consider using out of sample mean squared prediction errors (MSPEs) to evaluate the null that a given series...
When testing non-nested models, the asymptotic distribution theory of the ordinary like-lihood ratio statistic is not valid anymore. Several test statistics, some of them based on information criteria, have been proposed in order to test such non-nested hypotheses. Con-cerning bootstrap approaches...
2. Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance Machine learning 不适合投资的原因: 用预测方程,更容易overfitting 参数化方程,数据不确定 没有控制重复实验的次数(每次实验有不同的策略参数或者configuration) ...