پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 654 |
| تعداد مقالات | 9,577 |
| تعداد مشاهده مقاله | 68,502,980 |
| تعداد دریافت فایل اصل مقاله | 47,954,464 |
Big data analysis by using one covariate at a time multiple testing (OCMT) method: Early school dropout in Iraq | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 931-938 اصل مقاله (510.75 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5149 | ||
| نویسندگان | ||
| Ahmed Mahdi Salih* 1؛ Munaf Yousif Hmood2 | ||
| 1Department of Statistics, College of Administration and Economics Administration and Economics, Wasit University, Wasit, Iraq | ||
| 2Department of Statistics, College of Administration and Economics Administration and Economics, University of Baghdad, Baghdad, Iraq | ||
| تاریخ دریافت: 23 فروردین 1400، تاریخ بازنگری: 09 خرداد 1400، تاریخ پذیرش: 04 خرداد 1400 | ||
| چکیده | ||
| The early school dropout is very significant portents that controls the future of societies and determine the nature of its elements. Therefore, studying this phenomenon and find explanations of it is a necessary matter, by finding or developing appropriate models to predict it in the future. The variables that affect the early school dropout Iraq takes a large size and multiple sources and types due the political and economic situation , which attributes it as a sort of Big Data that must be explored by using new statistical approaches. The research aims at using one Covariate at a Time. Multiple Testing OCMT Method to analyze the data from surveys collected by the Central Statistical Organization IRAQ, which contains many indicators related to school dropout and meaningfully affect the life of the Iraqi persons. The Ridge Regression Method as well as the OCMT method were chosen to analyze data and the Mean Square Errors MSE was used to compare the two methods and From the results we find that OCMT estimator is better than Ridge estimator with Big Data conditions. | ||
| کلیدواژه‌ها | ||
| Big data؛ OCMT؛ ridge regression؛ multiple testing | ||
| مراجع | ||
|
[1] D. Acharjya, A. Kauser, A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools, International Journal of Advanced Computer Science and Applications. 2 (2016) 59-518. [2] W. Chang and N. Grady, NIST Big Data Interoperability Framework, Volume 1, Definitions, Special Publication (NIST SP) - 1500-1 Version 2, 2012 [3] A. Chudik, G. Kapetanios and M. Pesaran, One-Covariate at Time, Multiple Testing Approach to variable selection in High-Dimensional Regression Models, Econometrica, 4 (2018) 1479-1512. [4] A. Dorugade, New Ridge Parameters for Ridge Regression, Journal of the Arab Universities for Basic and Applied Statistics, 3 (2014) 94-99. [5] J. Fan, H. Fang, Challenges of Big Data Analysis, National Science Review, 1 (2014) 293-314. [6] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction , Springer Series in Statistics, USA, 2010. [7] A. Hoerl and A. Kennard, Ridge Regression: Biased Estimation for Nonorthogonal Problems Technometrics, 1 (1970) 55-67. [8] G. Kapetanios, M. Marcellino and K. Petrova, Analysis of the Most Recent Modeling Techniques for Big Data with Particular Attention to Bayesian Ones, Eurostat. Statistical working papers. ISBN 978-92-79-77350-1, 2018. [9] J. Lv, Y. Fan, A Unified Approach to Model Selection and Sparse Recovery Using Regularized Least Squares, The Annals of Statistics, 4 (2009) 3498-3528. [10] R. Tibshirani, Regression Shrinkage and Selection via the Lasso, Jornal of Royal Statistics Society. 1 (1996) 1456-1490. | ||
|
آمار تعداد مشاهده مقاله: 15,961 تعداد دریافت فایل اصل مقاله: 7,400 |
||