Discussion of ‘Statistical inference for streamed longitudinal data’

原文摘要

We congratulate the authors for their contributions to statistical analysis for streamed longitudinal data. Streamed data occur frequently in many modern biomedical areas such as mobile health, drawing increasing attention in recent years. Luo et al. (2023) developed a new statistical inference framework for streamed longitudinal data via the quadratic inference function approach. The proposed method avoids retrospective calculations using the individual-level data in the previous data batches, and therefore reduces the memory and computational burdens of storing and analysing the entire cumulative dataset. In their work, the authors focused on the case in which the dimension of the regression parameter β is small and fixed. In this discussion, we comment on the statistical inference for high-dimensional streamed longitudinal data. There is a large literature on how to construct confidence intervals and hypothesis tests for high-dimensional generalized linear models via debiased or decorrelated estimators, for instance, Javanmard & Montanari (2014), Van de Geer et al. (2014), Zhang & Zhang (2014), Belloni et al. (2016), Cai & Guo (2017), Ning & Liu (2017), Ning et al. (2017) and Neykov et al. (2018). Recently, Fang et al. (2020) further extended the decorrelated method to deal with longitudinal data in the offline setting via the quadratic inference function. In the following, we briefly outline the main idea on how the decorrelated method can be adapted to make inference for high-dimensional streamed longitudinal data.

摘要来源：OpenAlex 索引摘要（稿件版本未注明）