A comparison of some estimation methods for quantile regression model / (Record no. 171095)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06042namaa22004211i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20250414100614.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250305s2024ua |||a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| 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 | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 519 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.18.04.M.Sc.2024.Ma.C. |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Marwa Rateb Saad El-sayed Elseihy, |
| Preparation | preparation. |
| 245 12 - TITLE STATEMENT | |
| Title | A comparison of some estimation methods for quantile regression model / |
| Statement of responsibility, etc. | By Marwa Rateb Saad El-sayed Elseihy; Supervised by Prof. Ahmed Hassan Youssef, Dr. Shereen Hamdy Abdel-latif. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | مقارنة بين بعض طرق التقدير لنموذج انحدار الكونتيل / |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 130 Leaves : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc.)-Cairo University, 2024. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 81-85. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The least-squares estimator has several disadvantages when dealing with heteroscedasticity. Hence, this estimate will not be a Best Linear Unbiased Estimator (BLUE). Consequently, Quantile Regression (QR) has been used instead of linear regression. Quantile regression, by nature, is an extension of linear regression used when the conditions of linear regression are not met. The main objective of this thesis is to compare linear regression and quantile regression regarding the problem of heteroscedasticity. For this goal, the study has conducted a simulation study and empirical application of Corona Virus Disease (COVID-19) data. For estimating the quantile regression model based on linear programming, three algorithms are mainly used: Simplex, Interior Point, and Smoothing. Moreover, five Bootstrap approaches have been used to estimate the standard error of the coefficients with heteroscedasticity. In the light of the simulation and application study on COVID-19 data, it has been concluded that quantile regression is better than linear regression in both predicting errors and representing data in the presence of heteroscedasticity. In addition, the performance of the smoothing algorithm was higher at the upper conditional quantiles. Furthermore, it was clear that the Wild Bootstrap approach was superior to the simplex algorithm and the performance of the Markov Chain Marginal Bootstrap (MCMB) approach was distinguished when estimating the interior point algorithm. Also, it was noted that the performance of Paired Bootstrap (PB) and Generalized Bootstrap (GB) with unit exponential weights were better when smoothing algorithm estimation is applied. It is worth noting that the simulation results are consistent with the application results. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | إن وجود مشكلة عدم تجانس البيانات والتي قد تؤدي بدورها إلى عدم تماثل البيانات يؤثر بالسلب على تقدير المربعات الصغرى ولن يصبح مقدر المربعات الصغرى أفضل مقدر خطي غير متحيز (BLUE) عند استخدام نموذج الانحدار الخطي البسيط. لذا اتجه الباحثون إلى استخدام نموذج انحدار الكوانتيل والاعتماد عليه كبديل لنموذج الانحدار الخطي إذا اتسمت البيانات بعدم التجانس، انحدار الكوانتيل هو امتداد للانحدار الخطي المستخدم عندما لا تتحقق شروط الانحدار الخطي.الهدف الرئيسى من هذة الرسالة هو مقارنة أداء كل من نموذجي الانحدار الخطي وانحدار الكوانتيل عند وجود مشكلة عدم التجانس ولهذا الهدف، تم اجراء محاكاة وتطبيقًا تجريبيًا لبيانات حقيقية لكوفيد-19. لتقدير نموذج انحدار الكوانتيل على أساس البرمجة الخطية، ، يتم استخدام ثلاث خوارزميات بشكل رئيسي: Simplex، Interior Point، Smoothing. علاوة على ذلك، تم استخدام خمسة مناهج Bootstrap لتقدير الخطأ المعياري للمعاملات مع عدم التجانس. في ضوء دراسة المحاكاة والتطبيق على ببيانات حقيقية لكوفيد 19، تم التوصل إلى أن الانحدار الكوانتيل أفضل من الانحدار الخطي في كل من التنبؤ بالأخطاء وتمثيل البيانات في ظل وجود مشكلة عدم تجانس البيانات. بالإضافة إلى ذلك، كان أداء خوارزمية Smoothing أعلى في الكميات الشرطية العليا من التوزيع 0.75, and 0.9))τ=. علاوة على ذلك، اتضح تفوق نهج Wild Bootstrap مع خوارزمية Simplex، وتميز أداء نهج Markov Chain Marginal Bootstrap عند التقدير بخوارزمية ال Interior Point، وكان اداء كل من Paired Bootstrap (XY) و (Generalized Bootstrap with unit Exponential Weights (WXY أفضل عند التقدير بخوارزمية ال Smoothing، ومن الجدير بالذكر أن نتائج المحاكاة تتفق مع نتائج التطبيق. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issued also as CD |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in English. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Probability and applied mathematics |
| Source of heading or term | qrmak |
| 653 #0 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Bootstrap |
| -- | Heteroscedasticity |
| -- | Linear Programming |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ahmed Hassan Youssef |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Shereen Hamdy Abdel-latif |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2024 |
| Supervisory body | Ahmed Hassan Youssef |
| -- | Shereen Hamdy Abdel-latif |
| 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 | Sara Salah |
| Reviser Names | Eman Ghareb |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 05.03.2025 | 90950 | Cai01.18.04.M.Sc.2024.Ma.C. | 01010110090950000 | 05.03.2025 | 05.03.2025 | Thesis |