THE QUANTILE GARCH-DISTRIBUTED LAG (QGDL) FRAMEWORK: A UNIFIED MODEL FOR HIGH-VOLATILITY TIME SERIES

Maryam Jumaa Mousa; Munaf Yousif Hmood

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Publication Date

Mon Sep 07 2026

Journal Name

Twms Journal Of Applied And Engineering Mathematics

Volume

16

Issue Number

9

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THE QUANTILE GARCH-DISTRIBUTED LAG (QGDL) FRAMEWORK: A UNIFIED MODEL FOR HIGH-VOLATILITY TIME SERIES

Quantile regression

GARCH

QARDL

Conditional heteroskedasticity

Volatility clustering

Maryam Jumaa Mousa

Munaf Yousif Hmood

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TWMS Journal of Applied and Engineering Mathematics | Volume: 16 Issue: 9

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Publication Date

Mon Nov 11 2019

Journal Name

Spe

Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis

neural network

strength

machine learning

ROP prediction

Upstream Oil & Gas

prediction

ANN model

input parameter

multiple regression analysis

artificial neural network

Farqad

Hassan

Ethar

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Abstract<p>Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.</p><p>The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame</p> ... Show More

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(7)

Publication Date

Sat Jan 01 2022

Journal Name

Turkish Journal Of Physiotherapy And Rehabilitation

classification coco dataset using machine learning algorithms

learning Machine

classification

MCOCO dataset

K Nearest Neighbor's

Stochastic Gradient Descent learning (SGD)

Logistic Regression Algorithm(LR)

and Multi-Layer Perceptron (MLP).

Rasool

Bushra

Raaid

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In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

Publication Date

Mon Jun 05 2023

Journal Name

Journal Of Economics And Administrative Sciences

Comparison of Poisson Regression and Conway Maxwell Poisson Models Using Simulation

Poisson regression

Conway-MaxWell Poisson

Maximum Likelinood Method

Mean square error

Akakie

Inverse Simulation.

انحدار بواسون

كونواي ماكسويل بواسون

طريقة الامكان الاعظم

متوسط مربعات الخطأ

الاكايكي

محاكاة المعكوس.

Sarah

Lamyaa

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Regression models are one of the most important models used in modern studies, especially research and health studies because of the important results they achieve. Two regression models were used: Poisson Regression Model and Conway-Max Well- Poisson), where this study aimed to make a comparison between the two models and choose the best one between them using the simulation method and at different sample sizes (n = 25,50,100) and with repetitions (r = 1000). The Matlab program was adopted.) to conduct a simulation experiment, where the results showed the superiority of the Poisson model through the mean square error criterion (MSE) and also through the Akaiki criterion (AIC) for the same distribution.

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(1)

Publication Date

Sun Feb 01 2015

Journal Name

Journal Of Economics And Administrative Sciences

Climate change and dust storms in Iraq / 'Baghdad', case study

الغبار العالق - الغبار المتصاعد -العاصفة الغبارية -التغير المناخي -القيم المفقودة - القيم الشاذة المتعددة - نماذج انحدار

Suspended dust

rising dust

dust storm

climate change

missing values

multiple outliers

regression models

فاطمة جاسم

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A dust storm in Iraq is a climatic phenomenon common in arid and semi-arid regions . The frequency of the occurrence has increased drastically in the last decade and it is increasing continuously .Baghdad city like the rest of Iraq is suffering from the significant increase in dust storms . In this research , the study of the phenomenon of dust storms for all types (Suspended dust , rising dust , dust storm) , and its relationship with some climate variables (Temperature , rainfall ,wind speed) .The statement of the impact of climate change on this phenomenon to Baghdad station for the period (1981 – 2012) . Time series has been addressing the phenomenon of storms and cli

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(1)

Publication Date

Thu Jul 02 2026

Journal Name

Innovative Construction And Petrochemical Technologies

Improving Arabic Text Classification Accuracy Using Lightweight NLP Techniques

Arabic natural language processing

Arabic text classification

TF-IDF method

Chi-Square feature selection

linear SVM classifier

Logistic Regression

BiLSTM

AraBERT

efficient NLP models

accuracy and efficiency trade-off

Rasha

Safana

Sarah

Reiam

Rusul

Liqaa

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Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo

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