电竞投注

09月18日 江苏高校优势学科概率统计前沿系列讲座之一百三十二

发布时间: 2020-09-17   浏览次数: 10

bao gao ren: zhangxinyu jiaoshou

baogaotimu: improve machine learning by model averaging

baogaoshijian: 2020nian9yue18ri(zhouwuxiawu5: 00 )

baogaodidian: jiangsushifandaxueshuxueyutongjixueyuanxueshubaogaoting(jingyuanlou1506shi)

zhangxinyujiaoshoujianjie:

2010nianzaizhongkeyuanxitongsuohuoboshixuewei,cengshitamuboshihouhepsuderesearch fellow,xianweizhongkeyuanshuxueyuxitongkexueyanjiuyuanyanjiuyuan。danrenqikan《journal of systems science and complexity》lingyuzhubian、qikan《statistical analysis and data mining》associate editor、qikan《xitongkexueyushuxue》he《yingyonggailvtongji》bianwei,shishuangfaxuehuishujukexuefenhuifulishizhang、guojitongjixuehuidangxuanhuiyuanhezhiyuanqingniankexuejia。xianhouzhuchiguojiazirankexuejijinweiyouxiuhejiechuqingnianyanjiujijinxiangmu,cenghuodezhongguoguanlixueqingnianjianghezhongkeyuanyouxiuboshixueweilunwendengjiangli。2020nian8yue,ruxuandishiliujiezhongguoqingniankejijianghuojiangrenxuanmingdan。

电竞投注baogaozhaiyao: this paper introduces novel methods to combine forecasts made by machine learning techniques. machine learning methods have found many successful applications in predicting the response variable. however, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. to further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. simulation studies show that the proposed machine-learning-based forecast combinations work well. in empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and the simple average method.


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