MARC details
| 000 -LEADER |
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| fixed length control field |
07049namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
EG-GICUC |
| 005 - أخر تعامل مع التسجيلة |
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| control field |
20260204102646.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260124s2025 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 |
519.5 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
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| Classification number |
519.5 |
| 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.Ka.C |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Authority record control number or standard number |
Kamar Seddeik Abd-Eltawab Shahein, |
| Preparation |
preparation. |
| 245 12 - TITLE STATEMENT |
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| Title |
A comparison between estimation methods of fractional time series model (ARFIMA) / |
| Statement of responsibility, etc. |
by Kamar Seddeik Abd-Eltawab Shahein ; Supervised Prof. Ahmed Amin El-Sheikh, Dr. Amal Mohamed Abdl-Fattah. |
| 246 15 - VARYING FORM OF TITLE |
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| Title proper/short title |
مقارنة بين طرق تقدير نموذج السلاسل الزمنية الكسرية (ARFIMA) |
| 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 |
397 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 392 -397. |
| 520 #3 - SUMMARY, ETC. |
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| Summary, etc. |
The Box-Jenkins strategy for (Autoregressive Integrated Moving Average), often known as <br/>ARIMA (p,d,q), has gained popularity due to its ability to predict time series with short memory. <br/>These models enable the prediction of future points in the series. However, these models do not <br/>support non-integer values for the differencing Box-Jenkins methodology for Autoregressive <br/>Integrated Moving Average models, often known as ARIMA (p,d,q), has gained popularity due <br/>to its ability to predict time series with short memory. <br/><br/>This study focuses on the Autoregressive Fractionally Integrated Moving Average models <br/>(ARFIMA), a generalization of the ARIMA (p, d, q) model, that incorporates long memory in <br/>time series data. Unlike ARIMA, ARFIMA allows the fractional differencing parameter to take <br/>values between -0.5 and 0.5, making it especially effective with large sample sizes. ARFIMA is <br/>widely applicable across various fields, including economics, environmental science, social <br/>sciences, and medicine, where it is used to forecast future values. <br/><br/>To ensure knowledge of the time series analysis with various models, this study included <br/>several chapters, and the following is an explanation of the structure of each chapter: <br/><br/>Chapter one introduces the basic concepts of time series and ARFIMA (p, d, q) models, <br/>including methods for identifying long memory through graphs and tests, as well as model <br/>evaluation criteria for selecting the best fit. Chapter two is divided into two sections: a literature <br/>review focused on the ARFIMA model itself, and another reviewing programming aspects and <br/>the historical development of the model. Chapter three discusses the properties of the ARFIMA <br/>(0, d, 0) model and explores various parametric and semi-parametric estimation methods for the <br/>differencing parameter d. Chapter four presents a simulation study covering four models, <br/>ARFIMA (0, d, 0), (0, d, 1), (1, d, 0), and (1, d, 1), with different parameters t, 𝜑, and θ, along <br/>with conclusions, future work, and detailed results. <br/><br/>The result obtained from the simulation is Classical MLE emerges as the best overall method <br/>for all criteria MSE of d̂,AIC and BIC. It consistently provides the most reliable model selection <br/>by effectively balancing model fit and complexity, making it ideal for choosing parsimonious <br/>models. |
| 520 #3 - SUMMARY, ETC. |
|---|
| Summary, etc. |
يُعد تحليل الذاكرة طويلة المدى أحد المجالات النشطة في الاقتصاد القياسي وبحوث السلاسل الزمنية، حيث تم تطوير العديد من الأساليب لتحديد وتقدير معامل الذاكرة الطويلة. ويُعد نموذج الانحدار الذاتي الكُسري المتكامل والمتوسطات المتحركة (ARFIMA) من أكثر النماذج استخدامًا لتمثيل السلاسل الزمنية ذات الذاكرة الطويلة، إذ يتضمن هذا النموذج معامل فرق كسري يُرمز له d.<br/>ولتحديد النموذج المناسب من نوعARFIMA، لا بد من تقدير هذا المعامل الكسري بدقة. وتوجد عدة طرق لتقدير هذا المعامل، يمكن تصنيفها بشكل عام إلى فئتين رئيسيتين: الأساليب شبه المعلمية، والأساليب المعلمية التي تقوم بتقدير جميع معلمات النموذج في خطوة واحدة، بما في ذلك معامل التكامل الكسري.<br/>في هذه الرسالة، تم استخدام مجموعة متنوعة من الطرق الشائعة لتقدير المعامل الكسري في نماذج ARFIMA أحادية المتغير. وتشمل هذه الطرق شبه المعلمية كلًا من: طريقة جيويك-بورتر-هوداك (GPH)، وطريقة المدى المعاد تحجيمه(R/S)، وطريقة المدى المعاد تحجيمه المُعدّلة(MR/S)، والطريقة المنعّمة لـ GPH والمعروفة باسمdSperio، إلى جانب الطريقتين المعلميتين: التقدير الدقيق باحتمالية العظمى (Exact MLE)، وطريقة الاحتمالية العظمى الكلاسيكية(Classical MLE).<br/>وقد تم اتباع أسلوب المحاكاة لتقييم أداء هذه الطرائق المختلفة عبر قيم متعددة لمعامل d وأحجام عينات مختلفة. وتمت مقارنة الأساليب من خلال ثلاثة معايير رئيسية: متوسط مربع الخطأ (MSE of d ̂) ، ومعياري المعلومات AIC وBIC.<br/>وقد أظهرت نتائج المحاكاة أن طريقة الاحتمالية العظمى الكلاسيكية تُعد الأفضل من حيث الأداء العام بين جميع طرق التقدير، إذ أظهرت تفوقًا ملحوظًا في أغلب السيناريوهات، مما يعكس مدى موثوقيتها وفعاليتها في تقدير معامل الفرق الكسري في مختلف نماذجARFIMA. |
| 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 |
Statistics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
الإحصاء |
| 653 #1 - INDEX TERM--UNCONTROLLED |
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| Uncontrolled term |
ime series |
| -- |
long memory |
| -- |
short memory |
| -- |
ARFIMA (p,d,q) |
| -- |
Maximum likelihood |
| -- |
Hurst Exponent |
| -- |
Akaike information criteria. |
| -- |
السلاسل الزمنية |
| -- |
الانحدار الذاتي والمتوسطات المتحركة التكاملية |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Ahmed Amin El-Sheikh |
| Relator term |
thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Amal Mohamed Abdl-Fattah |
| Relator term |
thesis advisor. |
| 900 ## - Thesis Information |
|---|
| Grant date |
01-01-2025 |
| Supervisory body |
Ahmed Amin El-Sheik |
| -- |
Amal Mohamed Abdl-Fattah |
| 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 |
| 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 |