About Journal

Iraqi Journal of Statistical Sciences (IQJOSS) is a global, scientific and open access journal. This journal has been published twice a year by the College of Computer Science and Mathematics, University of Mosul, Iraq. The iThenticate is used to prevent plagiarism and to ensure the originality of our submitted manuscripts. A double-blind peer-reviewing system is also used to assure the quality of the publication. The Iraqi Journal of Statistical Sciences was established in 2001 and publishes original research, review papers in the field of Statistical Science, Mathematical and Computers....
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Application of Poisson's Hierarchical Regression Model to the Deaths of Covid-19 in Mosul City Hospitals

Ban Ghanem Al ani; Mahmmood Altai

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 1-14
DOI: 10.33899/iqjoss.2022.176199

This research deals with the study of the partial hierarchical Poisson regression model (with a random intercept), where this model is one of the most important models widely applied in analyzing data that is characterized by the fact that the observations take a hierarchical form. Where it the full maximum likelihood (FML) method is used to estimate the model parameters. The model was applied to the covid-9 deaths in Mosul city, were recorded during the period (1/1/202 - 1/9/2021), where four major hospitals in the city were selected to represent the group of second level of data (Ibn Sina Hospital, Al Salam Hospital, Shifa Hospital, General Mosul Hospital).
The research found the adequate of the model for this type of data, as it was found that there are some factors that contribute to the increase in the number of deaths in the epidemic, such as the advanced age of the patient, the length of stay in the hospital, the percentage of oxygen in the patient's blood, in addition to the incidence of some chronic diseases such as asthma. The study recommended a more in-depth study of other types of these models, and the use of other estimation methods, in addition to paying attention to the methods of data recording by the city health department.

Locally weighted regression for sunspots estimation and prediction

Ibtehal Faker Al-Dean Fadel; Muzahem Mohamad Al-Hashimi

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 15-23
DOI: 10.33899/iqjoss.2022.176200

Locally weighted regression (LOESS) is a modern non-parametric regression method designed for treating cases where classical procedures are not highly efficient or cannot applied efficiently. Sunspots are the darker areas of the solar sphere's surface relative to other regions and are an important indicator of solar activity .The aim of this paper is to model and predict the number of sunspots because of their very importance to understanding the terrestrial consequences of solar activity and its direct impact on weather and communication systems on Earth, which may lead to damage to satellites. In this paper, the number of sunspots represented by annual data for the period from 1900 to 2021 (122 years) as well as monthly data for the period from January 1900 to January 2022 (1465 months) was obtained from the global data center (Sunspot Index and Long-term Solar Observations) (SILSO). The LOESS regression used for estimating and predicting the number of monthly and annual sunspots. The smoothing parameter, as well as the degree of the polynomial that fulfills the lowest for Akaike corrected information criterion. The analysis showed the ability of the LOESS to represent sunspot data by passing diagnostic tests as well as its high predictive ability. From the predictive values for the monthly data, it found that the maximum average number of sunspots will be 123.7 in July 2022, and the lowest average will be in February with 61.3 sunspots. Regarding the annual data, it found from the predictive values that the maximum average number of sunspots will be in the year 2023 with an average of 161.7 sunspots, and the lowest average will be in the year 2029 with an average of 16.1.

Use the robust RFCH method with a polychoric correlation matrix in structural equation modeling When you are ordinal data

Omar Salem Ibraheem; Mohammed Jasim Mohammed

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 24-35
DOI: 10.33899/iqjoss.2022.176201

Structural Equation Modeling is a statistical methodology commonly used in the social and administrative sciences and all other.   In this research, the researcher made a comparison between methods of estimation Unweighted Least Squares with Mean and Variance Adjusted( ULSMV) and  weighted Least Squares with Mean and Variance Adjusted  (WLSMV). When we have a five-way Likert scale, the data is treated as ordinal using the polychoric matrix as inputs for the weighted methods with robust corrections. With robust standard errors ULSMV and WLSMV.No study compared these methods and the impact of outliers on them. where a robust algorithm is proposed to clean the data from the outlier, as this proposed algorithm calculates the robust correlation matrix Reweighted Fast Consistent and High Breakdown  (RFCH), which consists of several steps and has been modified by taking the clean data before calculating the RFCH correlation matrix, where these data are data clean from outlier to add in the methods and to calculate a correlation matrix for each method where the purpose is to keep the ordinal data to calculate the polychoric matrix, which is robust to the violation of the assumption of normal distribution.By conducting a simulation experiment on different sample sizes and the degree of distribution to observe the accuracy of the proposed method for obtaining clean data. On methods ULSMV and WLSMV before and after the treatment process by calculating the absolute bias rate For the standard errors and the estimated parameters, in addition to studying the extent of their effect on the quality of fit indicators for each of the chi-square index, Comparative fit index (CFI), Tucker-Lewis Index (TLI), and Root-Mean-Squared-Error-of Approximation( RMSEA), Standardized Root Mean square Residual (SRMR), , with the robust corrections in the chi-square index for each of the methods WLSMV and ULSMV the accuracy of the proposed.
 

Heuristic Approaches for Solving bi-objective Single Machine Scheduling Problems

Hawzhin Ahmad Salh; Ayad Mohamad Ramadan

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 36-41
DOI: 10.33899/iqjoss.2022.176202

In this research paper, n jobs have to be scheduled on one-machine to minimize the sum of maximum earliness and maximum tardiness. We solved a series of bi-criteria scheduling problems that related to minimize the sum of the maximum earliness and tardiness. Three new algorithms were presented, two for hierarchical objective and one for the simultaneous objective. Using the results of these algorithms, we minimize the sum of maximum earliness and maximum tardiness. This objective considered as one of the NP-hard problem, and it is also irregular, so this objective missed some helpful properties of regularity. The proposed algorithms had simple structures, and simple to implement. Lastly, they tested for different n.
 

Using ARIMA and Random Forest Models for Climatic Datasets Forecasting

Oday Aljuborey; Osamah Basheer Shukur

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 42-55
DOI: 10.33899/iqjoss.2022.176203

The climatic changes have important role which may lead to huge problems for the health of human and other organisms, therefore it is necessary to study and forecast this type of datasets to reduce .
 
the damages through planning and controlling for these changes in the future. The main problem can be summarized in the nonlinearity of climatic dataset and its chaotic changes. The common approach is the integrated autoregressive and moving average model (ARIMA) as traditional univariate time series approach. Therefore, more appropriate model for studying the climatic data has been proposed for obtaining more accurate forecasting, it can be called random forest (RF) model.This model cannot deal with nonlinear data correctly and that may lead to inaccurate forecasting results. In this thesis, climatic datasets are studied represented by minimum air temperature and rational humidity for agricultural meteorological station in Nineveh. This thesis aims to satisfy data homogeneity through different seasons and find suitable model deal with nonlinear data correctly with minimal forecasting error comparing to ARIMA as traditional model. The research found the adequate of the model for this type of data, as it was found that there are some factors that contribute to the increase in the number of deaths in the epidemic, such as the advanced age of the patient, the length of stay in the hospital, the percentage of oxygen in the patient's blood, in addition to the incidence of some chronic diseases such as asthma. The study recommended a more in-depth study of other types of these models, and the use of other estimation methods, in addition to paying attention to the methods of data recording by the city health department.

Statistical Analysis of Ordinal Response Variable: A Comparative Study

Liqaa Yaseen Alhamdany; Zaid Tariq Salah

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 56-67
DOI: 10.33899/iqjoss.2022.176204

Response variables in biological phenomena vary between three types: numerical response variables, ordinal categorical response variables, and nominal categorical response variables. In statistical studies, handling ordinal variables varies in accordance with the perspective of the statistical approach to the response variable. Ordinal variables can be adopted as nominal categorical variables, which neglect the ordinal property of the categories. Ordinal variables can also be treated.
as an ordinal categorical variable (discrete variable), in which case the ranking information can be utilized in establishing the predicted models. In this study, the most important statistical methods that can be used to analyze data with an ordinal response variable have been investigated. Among these methods are the Multiple Regression Method, and The Ordinal Logistic Regression Method. The mechanism of building models and parameter estimations were theoretically exhibited, as well as reading the statistical significance of the regression coefficients in all the models in the study. The application was carried out on a real sample of patients with osteoporosis. Where multiple models were built to determine the most important factors affecting the likelihood of developing the disease. The best model was diagnosed according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The results of the statistical analysis demonstrated the superiority of the ordinal logistic regression model over the multiple linear regression model in its explanation of the relationship between the response variable and the covariates.

Study of the two-parameter Weibull distribution and Estimation of the scale and shape parameter application to the voltage data of the cement material (review)

Safwan Nathem Rashed; Farok Thafer Thafer; Ali Abdallah Sulayman

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 68-77
DOI: 10.33899/iqjoss.2022.176206

Survival distributions are important and commonly used in different fields and the Weibull Distribution is one of these distributions that has a different formula and the Weibull distribution has been chosen with two parameters, the measurement parameter and the shape parameter, and its properties have been studied and the two distribution parameters have been estimated in two ways, namely the method of greatest possibility and the method of Biz when the information about the parameter is not available (Non-Informative) and when the information about the parameter is available (Informative),  What was reached in theory was applied to real data represented by a potentiometer of cement material for seven days.

Constructing a Multilevel Modeling to High-Resolution CT (HRCT) Lung in Patients with COVID-19 Infection

Didar Ahmad Rashid; Mohammad Faqe

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 78-90
DOI: 10.33899/iqjoss.2022.176224

The coronavirus disease, also called COVID-19 is caused by the SARS-CoV-2 virus. Most the people contaminated with the virus will experience mild to moderate symptoms of respiratory diseases. The aim of this paper is constructing a model by multilevel modeling for these patients who sufferers by coronaviruses, we got seven hospitals which totals (636) patients in private and public that 27% from Erbil, 26% from Sulaimani, 23% from Duhok and 24% from Halabja from the period (September 1th, 2019 to February 1th, 2022). In these modelling of multilevel restricted maximum likelihood estimation (RMLE) and full maximum likelihood (FML) acclimate estimate the parameters of multilevel models (fixed and random). The application was on the HRCT lungs of patients, seven hospitals were selected randomly among the county in Kurdistan region of Iraq. The result shows that all three variables are significant at the hospital level, but in the two final models add level-2 predictor (Doctor Experience) that interaction with level-1 predictor (smoker), which is far from significant. However, there is a significant relationship between being a diabetic and having a CT scan, but the relationship between smoking and having a CT scan is not significant.
 

Use The Coiflets and Daubechies Wavelet Transform To Reduce Data Noise For a Simple Experiment

Mahmood Mohamad Taher; Sabah Manfi Redha

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022, Volume 19, Issue 2, Pages 91-103
DOI: 10.33899/iqjoss.2022.176225

In this research, a simple experiment in the field of agriculture was studied, in terms of the effect of out-of-control noise as a result of several reasons, including the effect of environmental conditions on the observations of agricultural experiments, through the use of Discrete Wavelet transformation, specifically (The Coiflets transform of wavelength 1 to 2 and the Daubechies transform of wavelength 2 To 3) based on two levels of transform (J-4) and (J-5), and applying the hard threshold rules, soft and non-negative, and comparing the wavelet transformation methods using real data for an experiment with a size of 26 observations. The application was carried out through a program in the language of  MATLAB. The researcher concluded that using the wavelet transform with the Suggested threshold reduced the noise of observations through the comparison criteria.

Statistical Analysis of Geometric Stochastic Process with Application

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2012, Volume 12, Issue 1, Pages 166-188
DOI: 10.33899/iqjoss.2012.60242

يُعَدُّ موضوع العملية التصادفية الهندسية من المواضيع التي تلاقي اهتماما كبيراً وواسعاً لدى العديد من الباحثين في الوقت الحاضر لما له من أهمية في شتى المجالات المختلفة. وتُعَدُّ العملية الهندسية تعميماً لعملية التجديد.
لقد خُصِصَ هذا البحث لدراسة العملية الهندسية وتحليلها ، والمقارنة بين نموذج العملية الهندسية ونموذج عملية التجديد، فضلاً عن تقدير الدالة الهندسية من خلال استخدام الحل العددي والحل التقريبـــــي. وتضمن البحث تطبيقاً واقعياً لحالات الإصابة بالتهاب الكبد الفيروسي نوعي A وB في محافظة نينوى. وقد تم إجراء تحليل إحصائي للعملية الهندسية، واختبار ملاءمَة البيانات للعملية الهندسية، وتم تقدير العدد المتوقَّع لحالات الإصابة بالتهاب الكبد الفيروسي نوع A وB مع المقارنة بين طرائق التقديـــر.
وقد بين البحث أن العملية الهندسية ملائِمةُ للبيانات وللأنواع كافة، وأنّ نموذج العملية الهندسية أفضل من نموذج عملية التجديد. كما توصل إلى أن العدد المتوقَّع لحالات الإصابة بالتهاب الكبد الفيروسي باستخدام الحل العددي أفضل وأكثر دقةً من استخدام الحل التقريبي في التقدير.

Analysis and Modeling Time Series of Water Flow into Mosul City A Comparative Study

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2010, Volume 10, Issue 2, Pages 1-32
DOI: 10.33899/iqjoss.2010.28410

This paper presents fits for neural network model , and comparative resulting forecasts with those obtained from Box-Jenkins Method. We use time series data of Tigris's monthly flow into Mosul city from 1950-1995. To perform a comparative . forecasting work through the Box-Jenkins and neural network doesn't mean working with two different or competing aspect ; on the contrary choosing a proper architecture of neural net works requires using the skills of statistical modeling . As for application , Box-Jenkins Method has given more appropriate forecasts than those given by feed forward artificial neural network . We used Minitab and SPSS programs in the statistical aspect and Alyuda program in the neural network aspect.

New CG Method for Large-Scale Unconstrained Optimization Based on Nazareth theorem

Khalil . K. Abbo

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2008, Volume 8, Issue 1, Pages 53-65
DOI: 10.33899/iqjoss.2008.31676

In this paper we present new conjugate gradient method for computing the minimum value of differentiable real valued function in n variables ,this method derived from Nazareth theorem , which uses the equivalence of CG and Qusi–Newton methods on quadratic function also the descent property and Conjucy conditions are proved and compared with some well know CG method showing considerable improvement .

Using Markov Chains in Medical Field

Abd Al-Gafoor G. Al-Obeady; Ammar Y. Suleman

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2018, Volume 15, Issue 1, Pages 107-120
DOI: 10.33899/iqjoss.2018.159249

تم فی ىذا البحث د ا رسة السمسمة الزمنیة لعدد الإصابات بمرض ذات الرئة کمتسمسمة
مارکوف ، وذلک بوضع افت ا رضات عمى عدد الإصابات لصیاغة المسألة وفق نموذج متسمسمة
مارکوف بالاعتماد عمى عدد الحالات التی تمثل الظاىر. وبعد إیجاد الصفات الإحصائیة لیذه السمسمة
تبین أنیا ثبوتیة ) Ergodic ( ، وتم إیجاد التوزیع المستقر ) Stationary distribution ( لیذه
السمسمة .

Create Hybrid System (Artificial Neural Networks and Fuzzy System) tp Arrange Websites According to there Quality

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2010, Volume 10, Issue 1, Pages 473-494
DOI: 10.33899/iqjoss.2010.29144

The main of this study is to give model to estimate Websites quality by Artificial Neural Network and fuzzy system to arrange these websites according to there quality depending on the values that get it during the application of the model on these webs, this models contain the most important element suggested for quality criteria like the content quality, availability, reliability, performances and some other elements that must took care when estimating websites with possibility to apply it on all webs by using numeral numbers to meager these categories, in order to get a syntactic model can depended on in estimating websites for easy access to good websites and notarization information can use it in scientific research

Comparison between the method of Fuzzy Control and Discriminate Function in the classification of some wells Nineveh

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2011, Volume 11, Issue 2, Pages 315-330
DOI: 10.33899/iqjoss.2011.27628

In this study, the construction of a Fuzzy Inference Control to determine the validity of the quality of ground water wells of some of Nineveh province, through a series of observations that were obtained from the areas under study. To illustrate the importance of the model it was compared with the Discriminate Function that classify the individual to the right community. The model , has proved a model of control Fuzzy high efficiency in the determination of the validity of each well is compared to a model Discriminate Function was the only classification of the wells under study where there it is valid or invalid to drink, as well as easy construction of the computational procedures required to build a Fuzzy Inference model compared to complex computational procedures required by the Discriminate Function. The configured model can be used in future to distinguish the quality of any groundwater wells based on qualitative characteristics of the waters.
Keyword: Fuzzy Control, Discriminate Function, groundwater.

Analysis and Modeling Time Series of Water Flow into Mosul City A Comparative Study

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2010, Volume 10, Issue 2, Pages 1-32
DOI: 10.33899/iqjoss.2010.28410

This paper presents fits for neural network model , and comparative resulting forecasts with those obtained from Box-Jenkins Method. We use time series data of Tigris's monthly flow into Mosul city from 1950-1995. To perform a comparative . forecasting work through the Box-Jenkins and neural network doesn't mean working with two different or competing aspect ; on the contrary choosing a proper architecture of neural net works requires using the skills of statistical modeling . As for application , Box-Jenkins Method has given more appropriate forecasts than those given by feed forward artificial neural network . We used Minitab and SPSS programs in the statistical aspect and Alyuda program in the neural network aspect.

Evaluating the Area of a Circle and the Volume of a Sphere by Using Monte Carlo Simulation

Abd Al- Kareem I. Sheet

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2008, Volume 8, Issue 2, Pages 48-62
DOI: 10.33899/iqjoss.2008.31429

In this reseach Monte Carlo simulation is used to calculate the area of any circle as well as the volume of any sphere numerically. Different functions are considered to test the performance of this method, in addition to some improvement to give more satisfactory results.

Using Markov Chains in Medical Field

Abd Al-Gafoor G. Al-Obeady; Ammar Y. Suleman

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2018, Volume 15, Issue 1, Pages 107-120
DOI: 10.33899/iqjoss.2018.159249

تم فی ىذا البحث د ا رسة السمسمة الزمنیة لعدد الإصابات بمرض ذات الرئة کمتسمسمة
مارکوف ، وذلک بوضع افت ا رضات عمى عدد الإصابات لصیاغة المسألة وفق نموذج متسمسمة
مارکوف بالاعتماد عمى عدد الحالات التی تمثل الظاىر. وبعد إیجاد الصفات الإحصائیة لیذه السمسمة
تبین أنیا ثبوتیة ) Ergodic ( ، وتم إیجاد التوزیع المستقر ) Stationary distribution ( لیذه
السمسمة .

The Relationship Between Analysis Of Variance And The Regression Analysis Of Dummy Variables

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2008, Volume 8, Issue 2, Pages 113-132
DOI: 10.33899/iqjoss.2008.31101

Regression analysis of dummy variables with effect code showed the same results of the analysis of variance table when they applied on three fixed model designs. The designs were: completely randomized, randomized complete blocks and Latin square. This procedure gave an advantage upon the classic one, it yield additional information about the relation between the response and predictors as the regression analysis does, such as: coefficient of determination, identification of the outliers, also tackled the missing observations with out estimated them.

Estimation of Reliability Function for Complete Data - Article Review

Shaimaa Waleed

IRAQI JOURNAL OF STATISTICAL SCIENCES, 2019, Volume 16, Issue 3, Pages 25-38
DOI: 10.33899/iqjoss.2019.164172

The theory of reliability is one of the important theories that is attracting great interest in many researchers at present because of its importance in various fields of life especially in determining the age of a particular device and its efficiency and the addition of applications in the field of radar and atmospheric and astronomical observation. The growing interest in reliability studies is due to the rapid technical and technological development and the use of complex systems in various fields. This paper reviews the article of reliability in terms of the reliability function for complete data  of one of the failure time distributions (weibull distribution) and its parameters in different methods using simulation.

Publisher: University of Mosul

Email:  jstascimosul@gmail.com

Editor-in-chief: Assistant Professor Dr. Heyam A. Hayawi

Print ISSN: 1680-855X

Online ISSN: 2664-2956

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