摘要
应用Lasso方法研究平衡纵向数据模型的变量选择问题。通过Lasso方法可将模型的系数进行压缩并使之趋于零,甚至使一些系数等于零,利用LARS算法对回归系数进行排序,并采用AIC和BIC准则进行截取,从而达到变量选择的目的。同时证明该方法的一些理论特性,并从仿真模拟中分析了该方法的主要特点。作为实际应用,本方法可以有效地从众多的环境因素中寻找影响蝙蝠活动的主要因素。
The Lasso method is applied to study variable selection problem in balanced longitudinal data model. This method can shrink the coefficients toward to zeros, and even set some coefficients to zeros, then LARS algo- rithm is used to sequence the coefficients, and AIC and BIC criteria are used to select the tuning parameters. Fur- thermore, some theoretical properties are proved, and the characteristics of the approach are presented from some simulation results. As an application, this approach is applied to find out the main factors which have influence to the activities of bats effectively.
出处
《黑龙江大学自然科学学报》
CAS
北大核心
2012年第6期715-722,726,共9页
Journal of Natural Science of Heilongjiang University
基金
国家自然科学基金重点资助项目(31030011)
国家自然科学基金面上资助项目(30870371)
东北师范大学人文学院青年教师科研基金资助项目(2010004)
