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Related Experiment Videos

Adaptive Debiased Lasso in High-Dimensional Generalized Linear Models with Streaming Data.

Ruijian Han¹, Lan Luo², Yuanhang Luo¹

¹Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Kowloon, Hong Kong.

Journal of the American Statistical Association

|July 15, 2026

Summary

This study introduces an efficient online inference method for high-dimensional generalized linear models. The Adaptive Debiased Lasso (ADL) estimator updates estimates in a single pass, reducing computational complexity for real-time data analysis.

Keywords:

Confidence interval Lasso One-pass algorithm Stochastic gradient descent

Related Experiment Videos

Area of Science:

Statistics
Machine Learning
Computational Statistics

Background:

Traditional statistical inference relies on static datasets, limiting real-time analysis.
Online inference methods often require full dataset access or extensive summary statistics storage.
High-dimensional generalized linear models present computational challenges for sequential data.

Purpose of the Study:

To develop a novel online inference approach for high-dimensional generalized linear models.
To enable real-time updates of regression coefficients and standard errors with new data.
To reduce both time and space complexity compared to existing methods.

Main Methods:

A single-pass online inference algorithm.
An adaptive stochastic gradient descent algorithm for dynamic objective functions.
A novel online debiasing procedure to control optimization errors.

Main Results:

The proposed Adaptive Debiased Lasso (ADL) estimator maintains low-dimensional summary statistics.
Asymptotic normality of the ADL estimator is established.
Extensive simulations demonstrate the statistical validity and computational efficiency of the ADL estimator.

Conclusions:

The ADL estimator offers a computationally efficient and statistically valid approach for online inference in high-dimensional generalized linear models.
The method is effective for real-time analysis of sequentially collected data.
Demonstrated efficiency in spam email classification highlights practical applicability.