MARC details
| 000 -LEADER |
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| fixed length control field |
07796namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
EG-GICUC |
| 005 - أخر تعامل مع التسجيلة |
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| control field |
20260312145719.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260312s2025 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloguing agency |
EG-GICUC |
| Language of cataloging |
eng |
| Transcribing agency |
EG-GICUC |
| Modifying agency |
EG-GICUC |
| Description conventions |
rda |
| 041 0# - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| Language code of summary or abstract |
eng |
| -- |
ara |
| 049 ## - Acquisition Source |
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| Acquisition Source |
Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
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| Classification number |
006.31 |
| Edition number |
21 |
| 097 ## - Degree |
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| Degree |
M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
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| Local Call Number |
Cai01.18.04.M.Sc.2025.Om.P |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Authority record control number or standard number |
Omar Ahmed Mohamed Ahmed Afifi, |
| Preparation |
preparation. |
| 245 10 - TITLE STATEMENT |
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| Title |
Panel data analysis using supervised machine learning techniques / |
| Statement of responsibility, etc. |
by Omar Ahmed Mohamed Ahmed Afifi ; Supervised Prof. Salah Mahdy Ramadan, Dr. Amal Mohamed Abdel Fatah. |
| 246 15 - VARYING FORM OF TITLE |
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| Title proper/short title |
تحليل بيانات القطاع باستخدام تقنيات التعلم الآلي الخاضعة للإشراف |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
72 Leaves : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
CD. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rda content |
| 337 ## - MEDIA TYPE |
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| Media type term |
Unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (M.Sc)-Cairo University, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography: pages 64 -69. |
| 520 #3 - SUMMARY, ETC. |
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| Summary, etc. |
Panel data analysis allows researchers to achieve greater statistical validity in policy <br/>analysis and program evaluation through more advanced research designs than cross-sectional data <br/>models. Panel (or longitudinal) data refers to data collected from the same individuals across <br/>multiple time periods. This data type consists of repeated time-series observations (𝑇) for a <br/>significant number of cross-sectional units (𝑁), such as countries, companies, randomly chosen <br/>individuals, etc. <br/><br/>This thesis discusses a comparison between the three conventional models of panel data, <br/>referred to as statistical panel models (Pooled OLS, Fixed Effects, and Random Effects), and three <br/>of the supervised machine learning techniques (Support Vector Regression, Random Forest <br/>Regressor, and Gradient Boosting Regressor) that have been used in literature to model panel data. <br/>The comparison is done in terms of prediction performance by fitting each of the six models and <br/>calculating diagnostic metrics (MSE, Bias, AIC, and BIC), then comparing the different values of <br/>the models. <br/><br/>The first comparison is an empirical study that investigates the impact of education and <br/>experience on individual wages using panel data from Greene (2008). This dataset was analyzed <br/>using the six models: three classical statistical panel data models (POLS, FE, RE) and three <br/>supervised machine learning techniques (SVR, RFR, GBR). The empirical results show that the <br/>machine learning techniques outperform the statistical models across all evaluation metrics, <br/>including Mean Squared Error (MSE), Bias, Akaike Information Criterion (AIC), and Bayesian <br/>Information Criterion (BIC). Among the machine learning techniques, Gradient Boosting and <br/>Support Vector Regression achieve the most accurate and efficient fits. The statistical models <br/>exhibit relatively higher error and complexity, with the Fixed Effects model performing the worst <br/>due to its exclusion of important time-invariant regressors. <br/>The second comparison is based on a controlled simulation study using an assumed true <br/>data-generating process (DGP), evaluated across 16 combinations of cross-sectional units (𝑁 =<br/>10,50,100,200) and time periods (𝑇 = 10,50,100,200). Each scenario was simulated over 1000 <br/>iterations to obtain stable average metrics. The findings reveal that statistical panel data models – particularly Pooled OLS and Random Effects – consistently achieve near-zero bias across all <br/>configurations, while Fixed Effects suffers from persistent bias due to model misspecification. <br/>Meanwhile, machine learning techniques demonstrate superior performance in terms of predictive <br/>performance, achieving substantially lower Mean Squared Error (MSE), AIC, and BIC values, <br/>especially as the panel size increases. Among the ML models, Gradient Boosting consistently <br/>provides the most accurate and well-balanced results, highlighting its strength in capturing <br/>complex relationships in data rich panel structures. <br/><br/>The final part of the thesis recommends, for future work, exploring machine learning <br/>techniques other than the three used, introducing more values of 𝑁 and 𝑇 for simulation, doing <br/>simulation on different panel data settings (Unbalanced, Dynamic, etc.), and doing the simulation <br/>using different DGPs to determine whether the comparison results will change. |
| 520 #3 - SUMMARY, ETC. |
|---|
| Summary, etc. |
في هذه الرسالة تمت المقارنة بين ثلاث طرق تقليدية لتقدير بيانات البانل، تُعرف باسم نماذج البانل الإحصائية (الانحدار الخطي المجمّع، نموذج التأثيرات الثابتة، ونموذج التأثيرات العشوائية)، وثلاثة من تقنيات التعلم الآلي الخاضع للإشراف (انحدار المتجهات الداعمة، انحدار الغابة العشوائية، وانحدار التعزيز الاشتقاقي) التي استُخدمت في الأدبيات لنمذجة بيانات البانل. أُجريت المقارنة من حيث دقة التقدير عن طريق تركيب كل نموذج من النماذج الستة وحساب بعض المقاييس التشخيصية (مثل متوسط الخطأ التربيعي، مقياس التحيز، معيار أكايكي، ومعيار بيز)، ثم مقارنة القيم المختلفة للنماذج. تم إجراء المقارنة الأولى باستخدام مثال من بيانات حقيقية حول تأثير سنوات الخبرة والتعليم على أجور الأفراد العاملين. أظهرت النتائج التطبيقية أن تقنيات التعلم الآلي الثلاثة تفوقت بوضوح على نماذج البانل الكلاسيكية في جميع المقاييس التشخيصية. تمت المقارنة الثانية باستخدام بيانات محاكاة في 16 تركيبة مختلفة تم تنفيذ كل تجربة محاكاة على 1000 تكرار لضمان الاستقرار في حساب المتوسطات الإحصائية للمقاييس التشخيصية. أظهرت تقنيات التعلم الآلي تحيزًا أعلى في العينات الصغيرة، لكنه ينخفض بشكل ملحوظ مع زيادة حجم البيانات، حيث حقق انحدار التعزيز الاشتقاقي أفضل أداء من حيث تقليل التحيز عند أكبر أحجام العينة. كما تفوقت تقنيات التعلم الآلي على النماذج الكلاسيكية في متوسط الخطأ التربيعي، ومعياري أكايكي وبيز، خاصة عند تكبير حجم البيانات. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
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| Issues CD |
Issues also as CD. |
| 546 ## - LANGUAGE NOTE |
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| Text Language |
Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Machine learning |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
التعلم الآلي |
| 653 #1 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Panel Data Analysis |
| -- |
Statistical Panel Models |
| -- |
Pooled OLS |
| -- |
Fixed Effects |
| -- |
Random Effects |
| -- |
Panel Data using Machine Learning |
| -- |
Support Vector Regression (SVR) |
| -- |
Random Forest Regressor (RFR) |
| -- |
Gradient Boosting Regressor (GBR) |
| -- |
Panel Data Simulation |
| -- |
تحليل بيانات البانل |
| -- |
نماذج البانل الإحصائية |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Salah Mahdy Ramadan |
| Relator term |
thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Amal Mohamed Abdel Fatah |
| Relator term |
thesis advisor. |
| 900 ## - Thesis Information |
|---|
| Grant date |
01-01-2025 |
| Supervisory body |
Salah Mahdy Ramadan |
| -- |
Amal Mohamed Abdel Fatah |
| Universities |
Cairo University |
| Faculties |
Faculty of Graduate Studies for Statistical Research |
| Department |
Department of Applied Statistics and Econometrics |
| 905 ## - Cataloger and Reviser Names |
|---|
| Cataloger Name |
Shimaa |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| Edition |
21 |
| Suppress in OPAC |
No |