论文标题
线性拉索：位置模型分辨率
The Linear Lasso: a location model resolution
论文作者
论文摘要
我们使用位置模型方法来指导最小二乘分析可变选择和推理的套索问题。滋扰参数被视为选择解释变量的指标，而兴趣参数是响应变量本身。最近的理论通过在数据空间上的边缘化而消除了滋扰参数，然后使用所得的分布进行有关兴趣参数的推断。我们开发这种方法并发现：主要推论本质上是一维而不是$ n $维的；该推论的重点是响应变量本身，而不是最小二乘估计（随着变量的去除）；一阶概率可用；该计算相对容易；可以使用标量边缘模型；并且无效的变量可以通过分布倾斜或移位来删除。
We use location model methodology to guide the least squares analysis of the Lasso problem of variable selection and inference. The nuisance parameter is taken to be an indicator for the selection of explanatory variables and the interest parameter is the response variable itself. Recent theory eliminates the nuisance parameter by marginalization on the data space and then uses the resulting distribution for inference concerning the interest parameter. We develop this approach and find: that primary inference is essentially one-dimensional rather than $n$-dimensional; that inference focuses on the response variable itself rather than the least squares estimate (as variables are removed); that first order probabilities are available; that computation is relatively easy; that a scalar marginal model is available; and that ineffective variables can be removed by distributional tilt or shift.
