Using Quadratic Form Ratio Multiple Test to Estimate Linear Regression Model Parameters in Big Data with Application: Child Labor in Iraq

Authors

  • Ahmed Mahdi Salih
  • Munaf Yousif Hmood

DOI:

https://doi.org/10.33095/jeas.v28i131.2242

Keywords:

Big Data, OCMT, Multidimensional Poverty, Child Labor . OCMT

Abstract

              The current paper proposes a new estimator for the linear regression model parameters under Big Data circumstances.  From the diversity of Big Data variables comes many challenges that  can be interesting to the  researchers who try their best to find new and novel methods to estimate the parameters of linear regression model. Data has been collected by Central Statistical Organization IRAQ, and the child labor in Iraq has been chosen as data. Child labor is the most vital phenomena that both society and education are suffering from and it affects the future of our next generation. Two methods have been selected to estimate the parameter of linear regression model, one Covariate at a Time Multiple Testing OCMT. Moreover, the Euclidian Distance has been used as a comparison criterion among the three methods

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Published

2022-03-30

How to Cite

Salih, A. M. . and Hmood, M. Y. . (2022) “Using Quadratic Form Ratio Multiple Test to Estimate Linear Regression Model Parameters in Big Data with Application: Child Labor in Iraq”, Journal of Economics and Administrative Sciences, 28(131), pp. 167–179. doi: 10.33095/jeas.v28i131.2242.

Issue

Section

Statistical Researches