广义线性模型中带有测量误差的序贯压缩估计（英文）

Sequential shrinkage estimation in generalized linear models with measurement errors

对带测量误差的广义线性模型提出一种序贯压缩估计方法来确定最小样本量,使得在此最小样本量下所提方法可以选择有效变量,同时还可以获得给定精度下的回归参数估计.也研究了所提方法的渐进性质,包括序贯置信域的覆盖概率、最小样本量的效率等.模拟研究表明基于序贯压缩估计的抽样方法比传统的序贯抽样方法能够节省大量的样本.最后,用所提方法来分析一个糖尿病数据集.

A sequential shrinkage estimation method was developed to determine a minimum sample size under which both of the variable selection and the parameter estimation with a pre- specified accuracy were achieved for the generalized linear model with measurement errors. Asymptotic properties of the proposed sequential estimation method, such as the coverage probability of the sequential confidence set and the efficiency of the minimum sample size, were studied. Simulation studies were conducted and the results show that the proposed method can save a large number of samples compared to the traditional sequential sampling method. Finally a diabetes data set was used as an example.