Fine-Tuned Parallel Piecewise Sequential Confidence Interval and Point Estimation Strategies for the Mean of a Normal Population: Big Data Context

Nitis Mukhopadhyay () and Chen Zhang ()
Additional contact information
Nitis Mukhopadhyay: University of Connecticut, Department of Statistics
Chen Zhang: University of Connecticut, Department of Statistics

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 51-84 from Springer

Abstract: Abstract In this paper, we provide some new perspectives on sequential experimental designs for statistical inference in the context of big data. A fine-tuned parallel piecewise sequential procedure is developed for estimating the mean of a normal population having an unknown variance. With the help of such fine-tuning, asymptotic unbiasedness of the terminal sample size can be achieved along with the added operational efficiency as a result of utilizing the parallel processing or distributed computing. Theory and methodology will go hand-in-hand followed by illustrations from large-scale data analyses based on simulated data as well as real data from a health study.

Keywords: Big data; Fine-tuning; Fixed-width confidence interval (FWCI); Heart study; Minimum risk point estimation (MRPE); Normal population; Parallel piecewise sampling; Purely sequential sampling; Real data illustration; Second-order asymptotic efficiency; Simulations; Stopping rule (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_3

Ordering information: This item can be ordered from
http://www.springer.com/9783031071553

DOI: 10.1007/978-3-031-07155-3_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().