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2024 | OriginalPaper | Chapter

Fitting Big Data Using Structural Measurement Error Model

Authors : Saja Alzubai, Amjad D. Al-Nasser

Published in: Mathematical Analysis and Numerical Methods

Publisher: Springer Nature Singapore

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Abstract

The chapter delves into the challenges of analyzing big data, particularly the volume and complexity that exceed conventional database systems. It introduces the structural measurement error model (SMEM) and the subsampling algorithm as a solution to efficiently process big data. The author demonstrates how the sub-sampling algorithm can reduce computational time and improve estimator accuracy through hypothetical examples and Monte Carlo experiments. The chapter also highlights the robustness of the method under various data conditions, including the presence of outliers and non-normal distributions. By comparing AIC and BIC values, the author shows that the sub-sampling approach provides nearly identical statistical inferences to using the full dataset, but with significantly improved computational efficiency.
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previous chapter Efficient Construction Technique for Resolving Dirichlet Boundary-Value Problems in Multiply Connected Domains for Real Elliptic Equations
next chapter Stochastic Population Growth Model Using Three-Point Fractional Formula
Literature
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Wang, H.: More efficient estimation for logistic regression with optimal subsamples. J. Mach. Learn. Res. 20(132), 1–59 (2019)MathSciNet
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Wang, H., Yang, M., Stufken, J.: Information-based optimal sub-data selection for big data linear regression. J. Am. Stat. Assoc. 114(525), 393–405 (2019)CrossRef
Metadata
Title
Fitting Big Data Using Structural Measurement Error Model
Authors
Saja Alzubai
Amjad D. Al-Nasser
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Mathematical Analysis and Numerical Methods

Print ISBN: 978-981-9748-75-4

Electronic ISBN: 978-981-9748-76-1

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-4876-1_30

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