Sequential Sampling Methods for Statistical Inference
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 214
Special Issue Editors
Interests: sequential analysis; sampling strategies; u-statistics; applications in agriculture; economics; environmental health and tourism
Interests: survival analysis; copulas; competing risks; statistical decision theory; survival prognostic prediction; reliability; statistical process control; surrogate endpoints; meta-analysis; joint model
Special Issue Information
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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Keywords
- sequential sampling methods
- statistical inference
- point estimation
- interval estimation
- hypothesis testing
- change-point detection
- multi-armed bandits
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Statistical Inference on the Shape Parameter of Inverse Generalized Weibull Distribution
Authors: Yan Zhuang; Sudeep R. Bapat; Wenjie Wang
Affiliation: Connecticut College
Denison University
