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LIC criterion for optimal subset selection in distributed interval estimation.

Guangbao Guo1, Yue Sun1, Guoqi Qian2

  • 1School of Mathematics and Statistics, Shandong University of Technology, Zibo, People's Republic of China.

Journal of Applied Statistics
|June 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for interval estimation in linear regression with big data. The LIC criterion efficiently selects data subsets to reduce redundancy and improve computational feasibility.

Keywords:
62H1262J0568W15Distributed estimationLIC criteriondistributed linear regressionoptimal subset selection

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Area of Science:

  • Statistics
  • Computer Science
  • Data Science

Background:

  • Distributed interval estimation for big data is computationally challenging.
  • Existing methods may yield redundant information from distributed datasets.
  • Big data often resides across multiple servers or cloud environments, complicating analysis.

Purpose of the Study:

  • To develop an optimization procedure for selecting optimal data subsets for interval estimation.
  • To address the computational infeasibility of distributed interval estimation in linear regression.
  • To reduce information redundancy in distributed big data analysis.

Main Methods:

  • An optimization procedure was developed to select the best data subset.
  • The selection process is based on the LIC criterion, minimizing interval length and maximizing information.
  • Theoretical performance, simulations, and real-data analysis were used to evaluate the method.

Main Results:

  • The LIC criterion effectively selects informative data subsets for interval estimation.
  • The proposed method enhances computational feasibility for distributed big data.
  • The study demonstrates the practical applicability through real-world data analysis.

Conclusions:

  • The LIC criterion offers an efficient solution for distributed interval estimation in linear regression.
  • This approach mitigates computational challenges associated with big data.
  • The method provides a robust framework for analyzing large, distributed datasets.