1984年秋我在美国乔治城大学语言学系选听了美国著名语言教学法专家Earl W.Stevick教授的《语言教学法》课。他着重讲了形象和方法选择(Images and Options)两大问题。他所介绍的方法虽然许多教师都在使用,但一经归纳和分析却给人印象...1984年秋我在美国乔治城大学语言学系选听了美国著名语言教学法专家Earl W.Stevick教授的《语言教学法》课。他着重讲了形象和方法选择(Images and Options)两大问题。他所介绍的方法虽然许多教师都在使用,但一经归纳和分析却给人印象颇深。 Stevick教授归纳了二十余种可供课堂采用的方法。现在选择其中我个人认为适合我国课堂教学的二十种作一介绍。展开更多
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef...In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.展开更多
文摘1984年秋我在美国乔治城大学语言学系选听了美国著名语言教学法专家Earl W.Stevick教授的《语言教学法》课。他着重讲了形象和方法选择(Images and Options)两大问题。他所介绍的方法虽然许多教师都在使用,但一经归纳和分析却给人印象颇深。 Stevick教授归纳了二十余种可供课堂采用的方法。现在选择其中我个人认为适合我国课堂教学的二十种作一介绍。
基金Outstanding Youth Foundation of Hunan Provincial Department of Education(Grant No.22B0911)。
文摘In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.
