We propose a debiased stochastic gradient descent algorithm for online statistical inference with high-dimensional data. Our approach combines the debiasing technique developed in high-dimensional statistics with the stochastic gradient descent …
An online estimation and inference framework for streamed longitudinal data analysis (SLA).
Modern longitudinal data, for example from wearable devices, measure biological signals on a fixed set of participants at a diverging number of time points. Traditional statistical methods are not equipped to handle the computational burden of …
Two dominant distributed computing strategies have emerged to overcome the computational bottleneck of supervised learning with big data: parallel data processing in the MapReduce paradigm and serial data processing in the online streaming paradigm. …
An online two-way updating framework via mixed effects models to account for both within-site correlation and cross-site heterogeneity.
This paper presents an incremental updating algorithm to analyze streaming datasets using generalized linear models. The proposed method is formulated within a new framework of renewable estimation and incremental inference, in which the estimates …
A new framework of real-time estimation and incremental inference in generalized linear models with cross-sectional data streams.