Sequential Sampling Methods for Statistical Inference

Dear Colleagues,

In many statistical inference problems where achieving a predetermined level of accuracy is desired, the absence of fixed-sample-size procedures presents a challenge, as the required sample size often depends on some unknown nuisance parameters. To solve such problems, sequential sampling has proved helpful.

A defining characteristic of sequential sampling is that the number of observations is determined as the experiment goes on, allowing for conclusions to be reached earlier. Because of such efficiency, sequential sampling methods are developed and implemented in various areas such as machine learning, data mining, environmental monitoring, quality control, clinical trials, and finance.

This Special Issue focuses on recent advances in sequential sampling methods for statistical inference. Potential topics of interest for submission include but are not limited to, sequential point and interval estimation, sequential hypothesis testing, change-point detection, and multi-armed bandits. We invite researchers from diverse backgrounds to contribute original articles that address the importance of sequential sampling methods and their role in statistical inference.     

Dr. Jun Hu
Prof. Dr. Takeshi Emura
Guest Editors

Manuscript Submission Information

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