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

In: Artificial Intelligence, Big Data and Data Science in Statistics

Author

Listed:
  • Nitis Mukhopadhyay

    (University of Connecticut, Department of Statistics)

  • Chen Zhang

    (University of Connecticut, Department of Statistics)

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.

Suggested Citation

  • Nitis Mukhopadhyay & Chen Zhang, 2022. "Fine-Tuned Parallel Piecewise Sequential Confidence Interval and Point Estimation Strategies for the Mean of a Normal Population: Big Data Context," Springer Books, in: Ansgar Steland & Kwok-Leung Tsui (ed.), Artificial Intelligence, Big Data and Data Science in Statistics, pages 51-84, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-07155-3_3
    DOI: 10.1007/978-3-031-07155-3_3
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