异质更新回归的在线去偏拉索估计和推理

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Yajie Mi, Lei Wang
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引用次数: 0

摘要

在大数据时代,在线更新问题受到广泛关注。在实际应用中,模型的协变量集可能会随着数据流条件的变化而变化。本文针对中途添加新变量的高维异质线性回归模型,提出了一种两阶段在线去偏拉索估计与推理方法。在第一阶段,我们采用同质化策略,通过定义伪协变量和响应来表示异质模型。在第二阶段,我们进行在线去偏拉索估计程序,以获得最终估计器。从理论上讲,异质在线除杂套索估计器(HODL)的渐近正态性是成立的。通过模拟研究和真实数据实例,研究了所提估计器的有限样本性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Online debiased lasso estimation and inference for heterogenous updating regressions

Online debiased lasso estimation and inference for heterogenous updating regressions

In the era of big data, online updating problems have attracted extensive attention. In practice, the covariates set of the models may change according to the conditions of data streams. In this paper, we propose a two-stage online debiased lasso estimation and inference method for high-dimensional heterogenous linear regression models with new variables added midway. At the first stage, the homogenization strategy is conducted to represent the heterogenous models by defining the pseudo covariates and responses. At the second stage, we conduct the online debiased lasso estimation procedure to obtain the final estimator. Theoretically, the asymptotic normality of the heterogenous online debiased lasso estimator (HODL) is established. The finite-sample performance of the proposed estimators is studied through simulation studies and a real data example.

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来源期刊
Journal of the Korean Statistical Society
Journal of the Korean Statistical Society 数学-统计学与概率论
CiteScore
1.30
自引率
0.00%
发文量
37
审稿时长
3 months
期刊介绍: The Journal of the Korean Statistical Society publishes research articles that make original contributions to the theory and methodology of statistics and probability. It also welcomes papers on innovative applications of statistical methodology, as well as papers that give an overview of current topic of statistical research with judgements about promising directions for future work. The journal welcomes contributions from all countries.
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