However, for high-dimensionality data, those traditional methods are computationally expensive. In addition, ignoring stochastic errors in the variable selection process of previous steps is still a problem. 但是,对于高维数据,这些传统的选择方法需要大量的运算,而且忽视了在参数选择过程中每一步的随机误差。
The stochastic variable model of construction timing selection of rail transit projects based on real option was built up. 建立城市轨道交通项目建设时机选择的实物期权随机变量模型。