Iraqi Statisticians Journal (ISJ) is an open-access journal (without any fees) is published approximately semiannually by the Iraqi Association of Statistical Sciences. Its general objective is to promote and extend the use of statistical methods in all kinds of theoretical and applied disciplines. Iraqi Statisticians Journal (ISJ) is a global, scientific and open access journal. This journal has been published twice a year by the Iraqi Association of Statistical Sciences, Iraq. The Turnitin 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 Statisticians Journal was established in 2024 and publishes original research, review papers in the field of Statistical Science. ISJ is free of charge for publishing an accepted manuscript. The journal is financially supported by the Iraqi Association of Statistical Sciences. publishes original research papers and review papers. ISJ is dedicated to bringing together intellectual and professional scientists, as well as the public sectors who are engaged in efforts to understand the Statistical Science in the fields of: Statistics Mathematical Statistics Applied Statistics Stochastic Processes Regression Analysis Nonparametric Regression Generalized Linear Models Survival Analysis Spatial Statistics Multivariate Analysis Time Series and Forecasting Econometric Quality control High dimensional data
Iraqi Association of Statisticians Sciences
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
Bathtub Curve in Reliability Analysis. A Review
The bathtub curve is a popular concept and an important tool in reliability analysis. It describes the pattern or shape of the hazard rate function of products or systems throughout their life cycles over time. It helps in understanding failure modes and identifying critical periods in the systems life cycle in many fields such as engineering, quality management, electronics manufacturing, and other applied fields. This paper reviews the stages of the curve in some detail, along with everything that has been written by researchers on statistical estimation methods for the failure rate at each stage, highlighting the distributions related to this curve. It also addresses how to identify the points of change and transformation in this curve and the extent of its impact on preventive maintenance. The paper concludes with some critical points and objections to the bathtub curve.
Copyright (c) 2025 Suhad Ahmed Abdullah , Rabab Abdulrida Saleh (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Construction of an Almost Unbiased Estimator for Population Variance Using Exponential - Sine Type Estimator
In this paper, we have proposed a generalized almost unbiased estimator for estimating the unknown finite population variance of the study variable , using auxiliary information under the exponential-cum-sine estimator framework. In many applications, the presence of bias in an estimator can be a significant drawback. Following the procedure of Singh and Singh (1991, 1993), we develop an estimator based on a single auxiliary variable, which is almost unbiased up to the order . Expressions for the bias and mean squared error (MSE) of the proposed estimator are derived up to the first order of approximation. To support the theoretical results, an empirical study using two real-life data sets has been conducted. Additionally, a simulation study confirms that the proposed estimator has lower bias (nearly zero) and achieves a minimum MSE equivalent to that of the regression estimator.
Copyright (c) 2025 Sunil Kumar Yadav, Rajesh Singh (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Threshold Effects Between Inflation and Economic MicGrowth From Nigeria
This study investigates the threshold effects between inflation and economic growth in Nigeria, covering the period under review. Using annual macroeconomic data, the paper employs descriptive statistics, unit root tests, the Autoregressive Distributed Lag (ARDL) bounds testing approach, and threshold regression analysis. The ARDL results indicate evidence of a long-run relationship between inflation and growth, while the Error Correction Model (ECM) confirms short-run adjustments. Threshold regression analysis identifies a critical inflation level beyond which inflation exerts a significantly negative impact on economic growth. Specifically, the estimated threshold is around 13 percent, consistent with prior findings in the literature. Below this level, inflation has a mild positive or insignificant effect, whereas above the threshold, its adverse effects dominate. The results highlight the importance of maintaining inflation within manageable bounds to sustain growth. Policy implications suggest that Nigerian monetary authorities should strengthen inflation targeting frameworks and coordinate fiscal policies to achieve macroeconomic stability. The findings contribute to the ongoing debate on the inflation-growth nexus and provide empirical evidence relevant for policy formulation in developing economies.
Copyright (c) 2025 Alhaji Umar Abubakar, Imam Akeyede (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
The Evolution of Estimation Methods and Applications of the Fama-MacBeth Model: A Review
This literary review offers a comprehensive analysis of the evolution of methods for estimating and using the Fama-MacBeth regression model in research. The paper traces the model's development from a simple tool for testing the Capital Asset Pricing Model (CAPM) to a complex statistical framework used to analyze multifactor models and complex phenomena. The review focuses on the key developments in the uses of the Fama-MacBeth model and its estimation. It also highlights the expansion of the model's applications. In conclusion, this review shows that the Fama-MacBeth model remains a vital and effective tool in financial research, and its methodological and applied evolution has made it more powerful and flexible in facing the challenges of analyzing complex financial data.
Copyright (c) 2025 Mariam Jumaah Mousa, Munaf Yousif Hmood (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Estimation of Transition Probabilities in Multi-State Survival Data
This study calculates nonparametric estimators including Kaplan-Meier, Nelson-Aalen and Aalen - Johnson for applying the Multi-State Model (MSM ) to analyze type IIIness-death transitions between alive and death followed by different duct cancer types (Breast, Lung , Brain , Ovary) through reduction techniques by using diminutions (k+1)*(k+1) for matrix , The package software program was used to generate elementary Nelson-Aalen and Kaplan-Meier estimator, reach a conclusion that the two procedure are producing result that are very close..
Copyright (c) 2025 Haider kareem Raheem , Hayder yahya mohammed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
A proposal to Employ the Laplace Estimator and the IRWL Algorithm as Robust Methods in Segmented Linear Regression and Compare them with the Maximum Likelihood Estimator using Simulation
Segmented linear regression is one of the important statistical tools that is used to model and explain the behavior of some events in which changes gets suddenly or the behavior pattern of the event goes through transitional stages. The importance of this research lies in studying the proposal to employ the robust Laplace estimator within the method of estimating the parameters of the segmented linear regression model to give robustness to the estimation when there are outliers in the data. Then some updates were made to this method according to the iterative algorithm (IRWL) to get better robust estimates. On the practical side, the simulation experiment was conducted with several different sample sizes, and assuming several cases of pollution rates in the data (outliers) (15%, 10%, 5%, 0%), Then After implementing the simulation experiment and comparing the proposed methods with Muggeo's Maximum likelihood (ML) method using the (MSE) criterion, The experimental results showed the efficiency of the proposed methods when the data contains pollution ratios, and the iterative algorithm (IRWL) has proven its efficiency in obtaining the best estimate of the parameters compared to other methods. If there is no pollution in the data, the Muggeo's maximum likelihood (ML) method is the best for estimation.
Copyright (c) 2025 Mohammed Ahmed Abbas Al-Jubouri (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
A Generalized Shrinkage-type Estimator of Population Mean in Simple Random Sampling under conventional and non-conventional measures of auxiliary variables
In this study, a generalized shrinkage-type estimator of population mean in simple random sampling has been proposed. The proposed estimator is a combination of some of the known estimators in literature with the aim of obtaining estimators with higher efficiency. Its bias and mean squared error (MSE) have been derived using Taylor series up to the first order of approximation. The optimal MSE’s of the proposed class of estimators have been obtained. Theoretical comparison of the proposed shrinkage-type estimator has been also made with other existing related ratio estimators of the population mean using auxiliary information. The conditions under which the proposed shrinkage-type estimators perform better than the other existing estimators of population mean are given. Validation of results from both simulation and real data sets application reveals that the proposed shrinkage-type estimators performed better than some existing related ratio estimators considered in this work as they are having lower mean squared errors and higher percent relative efficiencies (PREs).
Copyright (c) 2025 Emmanuel J. Ekpenyong, Loveline Chiamaka Okoro, Theophilus Obijuru Nelson (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Evaluation of Relative Loss in Odd Generalised Exponential Burr Type X and Truncated Odd Generalised Exponential Burr Type X Distributions
This paper presents derivation entropy measures for two types of distributions: the Odd Generalised Exponential Burr type X distribution (OGEBX) and the truncated Odd Generalised Exponential Burr type X distribution (TOGEBX). The relative loss was achieved by testing a set of different parameter values. In the context of certain distributions, the optimistic results of this study can be applied. Based on the lowest value of each distribution, a numerical comparison was made between the entropy measures to select the optimal one. The relative loss was then determined using all six measures. When the OGE Burr Type X distribution is not used on [0, ∞], but the truncated OGE Burr Type X distribution on [0, t] is used.
Copyright (c) 2025 Murtadha M.Jasim, Moudher Kh. Abdal-hammed, Mizal Alobaidy, Alaa A. Khalaf (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Evaluating the Performance of Bayesian Estimates for Burr XII Inverse Rayleigh Parameters Using MCMC
This work applied multiple Bayesian approaches to estimate the parameters of the Burr XII Inverse Rayleigh (BXII-IR) Distribution. For Bayesian estimators, needed priors distribution for the parameters and certain loss functions such squared error, general entropy, and linear-exponential, owing to the unavailability of closed-form solutions for Bayesian estimates with these loss functions. Bayesian estimate employing the Markov Chain Monte Carlo (MCMC) approach were assessed for performance. The simulation results for Bayesian methods indicate that all methods consistently estimate the parameters, with the LN2 loss function estimator demonstrating the highest efficiency as assessed by mean squared error (MSE), root mean squared error (RMSE), bias, Highest Posterior Density Intervals (HPD), average interval length (AIL), and coverage probability (CP).
Copyright (c) 2025 Alaa A. Khalaf, Mundher A. Khaleel, Danial Mazarei, Ahlam H. Tolba, Akeem A. Adepoju (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Enhancing the Cosine-Lomax Distribution: A New Exponentiated Version with Improved Flexibility and Real-World Applications
In this paper, we introduce a novel flexible probability distribution called the Exponentiated Cosine Lomax distribution (ECLD), developed by compounding the exponentiated family with the cosine Lomax distribution. The proposed model incorporates an additional shape parameter, enhancing its flexibility to model complex real-world data with heavy tails, skewness, and non-monotonic hazard rates. We derive key statistical properties of the ECLD, including moments, moment-generating function, quantile function, and hazard rate. The model parameters are estimated using the maximum likelihood estimation (MLE) and maximum product of spacings (MPS) methods. A comprehensive simulation study is conducted to assess the consistency and efficiency of the estimators. To demonstrate the practical applicability of the ECLD, we analyze two real-world datasets. Comparative studies with existing models, including the Odd Frechet Lomax, half logistic Lomax, cosine Lomax, Lomax and sine Lomax distributions, reveal that the proposed ECLD provides a significantly better fit based on goodness-of-fit criteria such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Kolmogorov-Smirnov (K-S) test. The findings suggest that the ECL distribution is a robust alternative for modeling skewed and heavy-tailed data in various fields.
Copyright (c) 2025 Ibrahim Ali, Alhaji Modu Isa, Sule Omeiza Bashiru, Ibrahim Ismaila Itopa (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Survival Analysis of Acute Myocardial Infarction Patients Using the Kumaraswamy-Logistic Model and Kaplan-Meier Estimation
Acute Myocardial Infarction (AMI) remains a leading cause of morbidity and mortality worldwide, necessitating robust survival analyses to better understand prognostic factors and improve patient outcomes. This study applies the Kumaraswamy-logistic survival regression model and the Kaplan-Meier estimator to analyze survival trends in AMI patients using the Worcester Heart Attack Study dataset. The dataset comprises survival times for AMI patients, with censoring and covariates such as age, sex, and body mass index (BMI). The Kumaraswamy-logistic model provided maximum likelihood estimates (MLEs) of model parameters, revealing significant associations between survival time and specific covariates. Factors such as advanced age, male sex, and obesity were identified as significant risk factors negatively impacting survival. The Kaplan-Meier analysis revealed strong short-term survival probabilities, with survival rates at 98% and 79.6% at 6 and 187 days, respectively. However, long-term survival declined significantly, dropping to 15.8% by 2421 days. Increasing standard errors and widening confidence intervals in the Kaplan-Meier estimates underscored reduced precision in long-term survival predictions due to smaller sample sizes and higher variability. Comparative analysis between the two approaches showed that the Kumaraswamy-logistic model effectively captured the influence of covariates on survival probabilities, complementing the empirical insights from the Kaplan-Meier method. This study highlights the importance of integrating parametric and non-parametric methods for survival analysis to provide a comprehensive understanding of AMI survival trends and prognostic factors. The findings emphasize the need for targeted interventions to improve long-term outcomes, particularly for high-risk groups, and underscore the utility of advanced survival models in clinical decision-making and patient care strategies.
Copyright (c) 2025 Ibrahim Abubakar Sadiq, Yahaya Zakari, Sani Ibrahim Doguwa, Mannir Isiya, Suleiman Yahaya Suleiman, Ayodamola Hephzibah Ajayi, Yahaya Yahaya Gambo, Wisdom Samuel (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
A New Type I Half Logistic Topp-Leone Kumaraswamy Distribution: Properties and Applications to Covid 19 data
Various classical models have been generalized by many distribution theories to increase their flexibility and facilitate their use in various contexts. Families of distributions are mostly used to generalize and extend the classical models. Using family of distributions, a new four-parameter distribution named the type I half logistic Topp-Leone Kumaraswamy distribution, used in modelling real-life data sets, has been introduced. The new distribution is capable of modeling data with positively skewed and symmetric properties. Important statistical properties of the proposed distribution include the density function, hazard rate function, survival function, order statistics, probability weighted moments, moments, and moment generating function. The maximum likelihood estimation of the unknown parameters of the distribution has been obtained. Two real data sets relating to COVID-19 cases were employed to illustrate the usefulness and fit of the new model. The results showed that the type I half logistic Topp-Leone Kumaraswamy distribution provided better fits to the two data sets considered than the comparators used.
Type I half-logisTopp-Leone Kumaraswamy Distribution,
Copyright (c) 2025 Akeem Ajibola Adepoju, Sule Ibrahim, Bello O. A. , Usman Mohammed, Bukar S., Hamza M., Sani S. S. (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Modified Weibull-Fréchet Distribution Properties with Application
In this paper, we develop new statistical distribution from within the continuous distributions based on the Hybrid Weibull-G family and the Frechet distribution, called the Hybrid Weibull Frechet. This development comes by adding two new parameters to the basic distributions to give more flexibility and suitability to the basic Frechet distribution for reading and modeling real-world data. Also, present the basic functions of the new distribution (PDF, CDF). In addition, the paper will present an expansion of these functions and an expansion of the powers on which many statistical properties of the two proposed distributions will be built, such as the moments function, Incomplete Moments, Probability Weighted Moments, the survival function, the quantity function, and four types of entropy and disparity measures that represent a small number of... Numerous mathematical and statistical features of the developed distribution. The parameters were also estimated using the MLE. To obtain a distribution characterized by high flexibility to match different types of real data, a simulation was conducted to demonstrate the efficiency of estimating the unknown parameters of the new distribution using the maximum likelihood method. In addition, each distribution was tested on two types of real data of different sizes. The proposed new distribution proved highly flexible for the data when compared to other distributions using some statistical standards such as AIC, BIC, CAIC, and HQIC. The results showed the extent of flexibility and accuracy that supports what was discussed in the theoretical aspect.
Copyright (c) 2025 Rihab A. Abed, Mundher A. Khaleel, Nooruldeen A. Noori (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Properties of new extension of Exponentiated exponential distribution with Application
Lifetime data modeling is vital topic that has received extensive attention in the modern statistical literature. This study aims to present a new distribution resulting from the expansion of the Exponentiated Exponential distribution using the [0,1] Truncated Nadarajah – Haghighi – G family, and this distribution is named [0,1]TNHEE several mathematical properties of the new distribution are studied, including density function, cumulative distribution, survival function, hazard function, moments, moment generated function, entropies (Shannon Rényi, and Delta), Lorenz and Bonforoni function, residual function, in addition to rank statistic and maximum likelihood estimators. The distribution was applied to real world data representing the survival times of 72 guinea pigs infected with tuberculosis. The results showed that the new distribution outperformed several competing distribution, including [0,10TEEEE, BeEE, KuEE, EGEE, and WeEE using statistical comparison criteria such as AIC, BIC, CAIC, and HQIC, as well as the Kolmogorov – Smirnov test, which yielded a high p-value (0.603), confirming the goodness of fit. The results showed that the new distribution has high flexibility and excellent interpretability for real-world data, making it a effective candidate for medical and engineering applications.
Copyright (c) 2025 Mundher A. khaleel, Muhammad Sultan Namis, Hind Jumah Ahmed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
This study calculates nonparametric estimators including Kaplan-Meier, Nelson-Aalen and Aalen - Johnson for applying the Multi-State Model (MSM ) to analyze type IIIness-death transitions between alive and death followed by different duct cancer types (Breast, Lung , Brain , Ovary) through reduction techniques by using diminutions (k+1)*(k+1) for matrix , The package software program was used to generate elementary Nelson-Aalen and Kaplan-Meier estimator.
Hypothesis Testing for Non-Normal Multiple Compact Regression Model
Generalized multivariate transmuted Bessel distribution belongs to the family of probability distributions with a symmetric heavy tail. It is considered a mixed continuous probability distribution. It is the result of mixing the multivariate Gaussian mixture distribution with the generalized inverse normal distribution. On this basis, the paper will study a multiple compact regression model when the random error follows a generalized multivariate transmuted Bessel distribution. Assuming that the shape parameters are known, the parameters of the multiple compact regression model will be estimated using the maximum likelihood method and Bayesian approach depending on non-informative prior information. In addition, the Bayes factor was used as a criterion to test the hypotheses. A Gaussian distribution rule selects the bandwidth parameter and the kernel function based on the Gauss kernel function and quartic kernel function. It estimates the model parameters are under quadratic loss function. The researchers concluded that the posterior probability distribution of is a multivariate t distribution. Applying the findings to real data related to the jaundice percentage in the blood component as a response variable, red blood cell volume and red blood cell sedimentation as parametric influencing variables, and white and red cells as nonparametric influencing variables, the researchers concluded that when the shape parameters increase, the values ​​of the mean square error criteria of And the variance parameter decreases.
Generalized multivariate transmuted Bessel distribution
Jaundice percentage in the blood component data.
Copyright (c) 2025 Ons Edin Musa , Noor Saleem Mohammed Ali (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
New Stochastic Restricted Estimator: A Step Towards Improved Modeling in Linear Regression
Here, we propose a new convex linear combination estimator, called the New Mixed Estimator (NME), for multiple linear regression models with stochastic linear constraints on unknown parameters and multicollinearity between explanatory variables. The Ordinary Mixed Estimator (OME) and the Biased Stochastic Restricted Liu-type Estimator (SRLIT), two well-known estimators, are integrated to create the NME. Using Mean Square Error (MSE) as the main performance parameter, we examine the statistical characteristics of the NME and theoretically compare it to both OME and SRLIT to show its superiority. According to our research, the NME routinely performs better than the OME and SRLIT in terms of overall efficacy and statistical characteristics. Additionally, we provide a numerical example to clearly support these findings.
Copyright (c) 2025 Dounia J. Al-Obaidi, Mustafa Alheety (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Performance analysis of classical and fuzzy series components system of reliability estimation of New X-Lindley distribution simulation with comparative
This paper deal with analysis of classical and fuzzy series components systems of reliability shortly respectively R_s and R_Fs, based on estimation of these systems for New X –Lindley (NXL) distribution using eight different techniques. The techniques used include maximum likelihood estimation (MLE), weighted least squared (WLS), Approximate least squared (ALS), precise moments Method (PMM), percentile estimation (PE), and three shrinkage (Sh1, Sh2, and Sh3) techniques. Monte Carlo simulation is applied to test the performance of these techniques in estimating classical and fuzzy series distribution systems and analyze their accuracy according to criteria as mean absolute percentage error (MAPE), mean square error (MSE), and bias. The proofs of these distribution systems are presented, and then the extent of the impact of fuzziness in series components systems data on the estimation results is evaluated. This study founded that the performance of estimation techniques varied dependent on sample size, with MLE, Sh2, and Sh3 being the most accurate according to the evaluation criteria used. The results also showed that some traditional techniques, as WLS and ALS, suffer from greater bias when using fuzzy data, highlighting the impact of fuzzy estimation on the results of statistical models.
Copyright (c) 2025 Fajer Salah Ameer, Feras Sh. M. Batah2 (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Data Modelling and Analysis Using Odd Lomax Generalized Exponential Distribution: an Empirical Study and Simulation
This study is based on the formulation of a new probabilistic model called the generalized singular Lomax exponential distribution using the singular Lomax generator and the exponential distribution. The proposed distribution is very flexible in modelling age data with both decreasing and increasing (non-monotonic) shapes. We define the probability density function (pdf) and cumulative distribution function (CDF) for the proposed distribution. Some mathematical properties of the proposed distribution such as the quantum function and moments are derived and for the incomplete moments the Renyi entropy of the proposed distribution is also obtained. Furthermore, the paper discusses model parameters in three different techniques, with Monte Carlo simulations to determine the efficiency of estimating the HOLGE distribution, with a comparison with three benchmarks to determine the best estimation method. A practical application is also carried out on two types of data that include the survival times of 72 guinea pigs infected with virulent tuberculosis bacilli, where the efficiency of the HOLGE distribution analysis is determined by comparing it to six other distributions using 4 information criteria and 4 statistical measures, which demonstrated the efficiency and flexibility of the HOLGE distribution.
Copyright (c) 2025 Kamal Najim Abdullah, Nooruldeen Alsaab Noori , Mundher A. khaleel (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Estimating the Fuzzy regression panel data model based on approximate Bayesian computation with application
This study aims to estimate the parameters of fuzzy regression for panel data using a fixed regression model. To achieve greater accuracy in estimation, an approach is proposed that combines two estimation methods, leveraging the advantages of both probabilistic and traditional methods. The fixed regression model provides an integrated framework for analyzing cross-sectional and temporal data, contributing to a comprehensive analysis of panel data. This approach was applied to water pollution data of the Euphrates River using the fuzzy fixed regression model. The root mean square error (RMSE) criterion was used to compare different estimation methods. The results showed that the proposed methods generally outperformed the estimation methods, and gave better performance than probabilistic and traditional estimation methods. This study presents a new application for panel data using fuzzy regression, highlighting the benefit of combining traditional and probabilistic methods to achieve better estimations.
Copyright (c) 2025 Ahmed mutlag abdulateef, Emad Hazim Aboudi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
On the Theory and Pliability of Regressogram Decomposition: Application and Simulation Sensitivity of the Olanrewaju-Olanrewaju Kernel-Based Regressogram
This paper studies the pliability of regressogram decomposition as a technique of modeling bivariate random measurements of (X_1,Y_1),…,(X_n,Y_n) either via equal-width bins or linear smoother with the use of nonparametric, Olanrewaju-Olanrewaju, and machine-learning Boxcar kernels. The nonparametric, Olanrewaju-Olanrewaju, and Boxcar kernels were differently incorporated into the Nadaraya-Watson kernel-estimator as a generalized Mercer kernel. The optimal bandwidth needed as a smoothing parameter for impelling the kernel-based regressogram was derived with the aid of Generalized Cross-Validation (GCV). Finite and countable sample size bound required for the regressogram modeling was ascertained for reasonable sample sizes needed for effective optimization of coefficients, and deductive measurable of some error indexes via strongly universally consistent estimator. In conclusion, the Olanrewaju-Olanrewaju kernel-based regressogram was notably sensitive with the smallest magnitude of scale estimated (σ^2) and GCV estimates in application to real life dataset and simulation study to the nonparametric Doppler regression function.
Copyright (c) 2025 Rasaki Olawale Olanrewaju , Sodiq Adejare Olanrewaju (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Stability Analysis of Threshold Autoregressive Distributed Lag (TARDL(1,1,1)) Models Using Dynamical Approach: Applied Study of Limit Cycle Condition and Orbital Oscillations
This research aims to study the stability condition of the Threshold Autoregressive Distributed Lag [TARDL(1,1,1)] model using dynamic Approach. The limit cycle condition was applied to the model, where the stability of the model was analysed through differential equations that reflect oscillations and cycles, firstly will prove and present the limit cycle condition for this model. Two examples were presented: the first one satisfies the stability of limit cycle and stabilizes at a constant value, while the second one does at satisfy the condition and continuous to oscillate. MATLAB programing was used to plot the model trajectories and explain whether it is stable or not. The results showed that the model stabilizes when the condition is satisfied, while not satisfying it leads to continuous oscillations. Finally a practical application was conducted on real data to determine the stability or not of the models at the limit cycle.
Copyright (c) 2025 Eman Khalel Mohamed, Hiba H. Abdullah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Reliability Evaluation of a Hybrid Renewable Energy Complex System with Weibull Failure Laws Using Minimal-cut Set Approach
Most rural locations in Nigeria are still grappling with epileptic power supply while others await connection to national grid due to their remote locations. In order to meet the required energy demands at these locations, penetration of renewable energy sources has become so paramount to the society and the nation at large. Renewable energy sources are very attractive energy sources due to their abundant available nature, highly clean energy source because of their environmental friendly nature and cheap continuous nature. However, because of their intermittent available property, hybrid renewable energy sources are used to counter this drawback. In this study, hybrid renewable complex energy system with five components subsystem is evaluated. The complex system was first converted to a series-parallel system using the Minimal-cut set approach. The variables associated with repair and failure rates of each component is assumed to follow a Weibull probability density function. The derivation of Mean Time to System Failure (MTSF) and reliability of the system was obtained via reliability mathematics techniques. The MTSF and reliability of the complex renewable system was calculated for random values of component parameters such as failure rate , shape parameter and operating time (t) of the system components. The dynamic responses of these reliability indices have been presented both graphically and numerically with arbitrary numerical values of the components parameters.
Copyright (c) 2025 Ignatius K. Okakwu, Akintunde S. Alayande, Kehinde O. Orolu, Ayodeji A. Okubanjo, Blessing O. Orogbade, Samson J. Bukola, Ike C. Christian (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Extension of the Weibull Survival Regression Based on Sine Type II Topp-Leone-G Family of Distribution: Properties and Application
This study introduces the Sine Type II Topp-Leone Weibull (STIITL-W) distribution, a novel statistical model designed to enhance flexibility in data modeling. The proposed distribution extends the classical Weibull distribution by incorporating the Sine Type II Topp-Leone family, offering improved adaptability for complex datasets. Key mathematical properties are derived, including moments, moment-generating functions, and reliability measures. Parameter estimation is performed using the maximum likelihood estimation (MLE) method, and a simulation study is conducted to evaluate the consistency and efficiency of the estimates. Additionally, a Sine Type II Topp-Leone Weibull Survival Regression (STIITL-WSR) model is developed for survival data analysis. The model's performance is assessed using real-world datasets, including transect stake distance measurements and liver cancer survival data. Comparative analysis based on the Akaike Information Criterion (AIC) demonstrates that the STIITL-W distribution outperforms competing models, such as the Topp-Leone Modified Weibull, Exponential, and Generalized Weibull distributions. Furthermore, the log-STIITL-Weibull survival regression model achieves a lower AIC compared to the log-TLG-Weibull regression model, confirming its superior performance in survival data modeling.
Copyright (c) 2025 A. M. Isa, S. I. Doguwa, B. B. Alhaji3 , G. Dikko (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Employing Artificial Intelligence Algorithms to Predict the Financial Indicators of APPLE and NFLX Stocks
This research explores the use of artificial intelligence algorithms to improve the forecasting of financial indicators for APPLE and Netflix stocks. It aims to explore how financial markets can benefit from modern techniques to analyze financial data and make accurate predictions about future stock movements. The research included a number of cases depending on the Data-set source NFLX, APPLE recorded variable close price, high price, low price, open price, volume price), predict methods Intelligent Neural Networks, Honey Bee Algorithm Activation Function Sigmoid, Tanh, ReLU, number of bees 10,30,50. Results are compared using the mean square error MSE. Best result was for the case Honey Bee Algorithm, APPLE, high price, 30 with MSE= 0.0013. Other methods can be taken convolutional neural networks, random neural network.
Copyright (c) 2025 Mahmood jawad Abu Alshaeer, Waleed Abdullah Araheemah, Nazar Mustafa al-Sarraf (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Predicting the Factors Influencing Inflation in Iraq from (2016 – 2026)
Inflation is one of the most significant contemporary economic issues, which has preoccupied economists for past decades. It is defined as a continuous increase in the general price level over more than a year due to economic reasons, which affects purchasing power—meaning that rising prices lead to a reduction in the purchasing power of goods and services. Tackling inflation and maintaining price stability are primary objectives that governments aim to achieve.In this study, a simple regression model and a neural network were applied to forecast the inflation rate. The findings indicate an increase in inflation rates as a result of money supply growth. Data was collected from the Central Bank of Iraq for the years 2004–2016 and analyzed using SPSS statistical software and MatlabR 2019b.
Copyright (c) 2025 Fatimah Abdul – Hammeed jawad AL-Bermani (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Nonparametric Estimation for Nonstationary Time Series Models
This paper aimed to use some nonparametric methods in estimating nonstationary time series through the application of the cointegration regression methodology. The research employed both descriptive and econometric methods to construct the standard ECM (Error Correction Model) for monthly time series data for the period 2010-2015. The results relied on the Phillips-Perron unit root test to ascertain the stationarity of the time series and the Engle and Granger cointegration test to examine the existence of a long-run relationship. The application focused on the use of two nonparametric methods, in order to compare and identify the best method for estimating time series models in the light of the cointegration regression methodology. The results proved the superiority of the Lowess method over the cubic Spline method, as it achieved the shortest period and the highest adjustment ratio for disturbances occurring in the short run, with the aim of returning to the long run
Copyright (c) 2025 Mayson Abid Husseen, Munaf Yousif Hmood (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Estimation Methods: Inference Classical and Bayesian of Extended Inverse Exponential Distribution
The parameters of the Burr XII-Inverse Exponential (BXII-IE) distribution were estimated in this work using a variety of Bayesian and non-Bayesian (Inference Classical) approaches. Methods like Maximum likelihood, least squares, weighted least squares, Maximum product space estimators, and Anderson Darling were developed for non-Bayesian estimators. Due to the lack of closed-form solutions for Bayesian estimates for certain loss functions squared error, general entropy, and linear-exponential and prior distributions for the parameters, Bayesian estimators had to be implemented. Bayesian estimation utilizing the Markov Chain Monte Carlo (MCMC) approach and seven non-Bayesian estimate techniques were tested for performance.
Copyright (c) 2025 Alaa A. Khalaf, Mundher A. khaleel (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Estimation of the Parameters of the Binary Logistic Regression Model Using the Bootstrap Method and the Employment of the Genetic Algorithm for Epilepsy Patients
This research addresses a comparative study between the bootstrap and the Employment of the genetic algorithm methods, which are statistical and intelligence techniques used for estimating models based on resampling with replacement. They are considered important methods in estimating das Modell der logistischen Regression, welches eines der most commonly used modells in binary data analysis. These two methods were applied to the data of epilepsy patients, which is considered one of the most prominent global health issues. A random sample of 142 individuals was collected from Al-Hussein General Hospital in Dhi- Qar for the year 2023.The research aims to determine the most efficient method for estimating the logistic regression model by comparing the performance of the two methods. The importance of the study comes from the use of the bootstrap and genetic algorithm methods to obtain more accurate estimates, in addition to the significance of binary logistic regression in interpreting the relationship between independent variables and the dependent variable.The research relied on several criteria to measure the performance of the methods, the most prominent of which are the accuracy rate and the mean squared error. The maximum likelihood method was also employed to estimate the parameters of the model. The findings demonstrated that the genetic algorithm approach worked better in terms of accuracy, obtaining the lowest mean squared error, making it the best method for model estimation
Copyright (c) 2025 Asmaa Ghalib Jaber, Fahad Hussein Enad, Zainab Nihad Mohammed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/
Statistical Evaluation of the Gompertz-Fréchet distribution: Statistical Elasticity Analysis Using Simulation, Estimation and Application
This paper deals with the analysis of the Gompertz-Fréchet (GoFr) distribution, which combines the Gompertz family and the Fréchet distribution, where the statistical properties of proposed distribution are studied and accurate estimation methods for the distribution parameters are developed. Monte Carlo simulation was employed to evaluate the performance of three estimation methods: the Maximal likelihood method (MLE), the least squares method (LSE), and weighted least squares method (WLSE). The simulation included different sample sizes ranging from n=50 to 200, and the results were analyzed using criteria such as bias, mean square error (MSE), and root mean square error (RMSE). The results showed that the MLE method is the most accurate and efficient, as the bias and MSE decreased with the increase in sample size. The GoFr distribution was also applied to real data and compared with six other distributions using statistical accuracy criteria such as AIC and BIC. The results confirmed that the GoFr distribution is superior in its fit to the data compared to other models, which reflects its flexibility and effectiveness in analyzing data of complex nature.
Copyright (c) 2025 Ola Hammoodi, Hiba H. Abdullah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0/