SCOPUA Journal of Applied Statistical Research
SCOPUA Journal of Applied Statistical Research (JASR) ISSN 3104-4794 aims to serve as a comprehensive, peer-reviewed, open-access platform dedicated to advancing knowledge and fostering innovation in the field of applied statistical science. The journal publishes high-quality, original research articles, reviews, and case studies that explore the development and application of statistical methodologies to solve complex real-world problems. SCOPUA Journal of Applied Statistical Research, ISSN 3104-4794, strictly adheres to international publishing standards. COPE Code of Conduct Scopus Content Selection & Advisory Board (CSAB) DOAJ Principles of Transparency
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Socioeconomic and Demographic Study of the Determinants of Anaemia Prevalence Among Women in Nigeria
Anaemia remains a significant public health issue among women of reproductive age, especially in developing countries like Nigeria. This study examines the influence of socio-economic and demographic factors on anaemia among women in Nigeria using data from the 2018 Nigeria Demographic and Health Survey (NDHS). Variables analyzed include age, marital status, body mass index (BMI), education level, wealth index, pregnancy status, contraceptive use, region, place of residence, ethnicity, media exposure, and access to improved water. We first used descriptive statistics and Pearson's chi-square tests to explore links between women's anaemia status and key factors like residence, education, and wealth. Variables showing significant associations at this stage were then examined further with a partial proportional odds model, chosen to properly handle the ordered categories of anaemia severity (non-anaemic, mild, moderate, severe). Among the women surveyed, 41% had no anaemia, while 27% showed mild anaemia, 30% moderate anaemia, and 2% severe anaemia. Rural living, lower education evels, poorer household wealth, current pregnancy, lack of improved water access, and certain regional differences all raised the odds of severe anaemia significantly. The study concludes that socio-economic inequalities play a significant role in the burden of anaemia among women in Nigeria. Targeted interventions focusing on education, economic empowerment, and improved healthcare access are essential to reduce anaemia prevalence.
Copyright (c) 2026 Onyenekwe Chukwuenyem E, Oladuti Mercy. O , Adubiaro Dunsin , Onyenekwe Amala Joy (Author) https://creativecommons.org/licenses/by/4.0
Decomposition with Mixed Model when Trend-Cycle Component is Exponential
This paper discusses the decomposition of an observed time series data which admits the mixed model when trend-cycle component is exponential. Once the estimates of the trend-cycle and seasonal components, are obtained, estimates of the residuals or irregular component can be obtained from the observed series either by successive subtraction (for the Additive model) or by successive division (for the Multiplicative model) of the estimates of trend-cycle and seasonal components. However, for the mixed model, estimates of the residuals obtained either by successive subtraction from or by successive division of the observed series are contaminated by estimates of the estimates of trend-cycle and seasonal components. The purpose of this study is to show how estimates of residuals uncontaminated by the estimates of trend-cycle and seasonal components can be obtained. The Buys-Ballot procedure for time series decomposition was adopted in this study. The procedure is based on row, column and overall means and variances of the Buys-Ballot table. When trend-cycle component is exponential estimates of the trend-cycle component and seasonal indices are derived from these row, column and overall means of the Buys-Ballot table. Simulated series were used to illustrate results. Evaluation of the estimates of the residuals shows that the fitted models adequately describe the patterns in the simulated series. The proposed procedure has been recommended for decomposition of any observed series that admits the mixed model and exponential trend-cycle component.
Copyright (c) 2026 Eleazar Nwogu, Udoka Ikediuwa (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning-Based Survival Analysis and Recurrence Prediction in Breast Cancer Patients Using Clinical and Genomic Data
Reliable survival prediction is essential for personalised management of breast cancer, yet conventional models often fail to capture complex interactions among clinical, pathological, and genomic features. This study applied a deep learning framework (DeepSurv) to a cohort of 2,509 breast cancer patients, integrating clinical, histopathological, and genomic data to predict overall survival (OS) and relapse-free survival (RFS). Exploratory analysis revealed a median age at diagnosis of 60.4 years, a median tumour size of 26 mm, and a median of 0 positive lymph nodes, with a relapse rate of ~40%. DeepSurv demonstrated superior predictive performance compared to classical Cox regression, achieving C-index values of 0.7567 (OS) and 0.6495 (RFS) versus 0.7038 (OS) and 0.6403 (RFS) for Cox models. SHAP analysis identified positive lymph nodes, tumour grade, tumour size, age, and Nottingham Prognostic Index (NPI) as the most influential predictors, while mutation count and treatment variables contributed moderately. Survival curves indicated higher individualised survival probabilities with DeepSurv, reflecting improved sensitivity to patient-specific risk patterns. Classical Cox regression performed adequately but exhibited reduced discriminatory power, particularly for RFS. These findings demonstrate that deep learning models can integrate multi-modal data, enhance predictive accuracy, and maintain interpretability, supporting patient stratification and informed clinical decision-making. Future work should incorporate additional molecular and treatment-response data to improve relapse prediction further.
Copyright (c) 2026 Serifat Folorunso , Richard Oluwaseun Kehinde , Ibrahim Arionola Fayemi , Sukurat Salam (Author) https://creativecommons.org/licenses/by/4.0
Assessment of the performance of the Bayesian Forecasting for Vector ARMA Processes
Multivariate time series are widely observed in numerous domains. In economics, for example, you can monitor the annual savings in conjunction with the real interest rate. Such variables are jointly analyzed to understand the dynamic interactions that exist between them, thus improving the precision of forecasts. Enhanced forecasting is achievable when the series are examined together, especially when one series holds information about another one. An approximate Bayesian analytical method to estimate and forecast vector autoregressive moving average (Vector ARMA) processes was introduced by Shaarawy (1989). A basic goal for the research in hand is the numerical assessment of the proposed approach in tackling forecasting difficulties associated with Vector ARMA processes through a comprehensive simulation study. Furthermore, the research checks how the performance of the suggested method fluctuates when varying parameter values and time series lengths. The findings of the numerical study demonstrated that the methodology was effective in accurately forecasting future observations for Vector ARMA processes across various values of the parameter and different time series lengths.
Vector autoregressive moving average processes;
Copyright (c) 2025 SCOPUA Journal of Applied Statistical Research https://creativecommons.org/licenses/by/4.0
Modelling Wind Speed at Ikeja Station using Skewed Statistical Distributions
Wind speed, a key atmospheric parameter, results from the movement of air from high- to low-pressure regions driven primarily by temperature variations. This study modelled the wind speed (m/s) of Ikeja, Lagos State, using data from 2000 to 2020 through statistical techniques such as descriptive statistics, data visualization, and goodness-of-fit (GOF) tests, including chi-square, Kolmogorov-Smirnov, Anderson-Darling, and Cramer-von Mises analysis. Three positively skewed distributions, Gamma, Weibull, and Log-normal, were evaluated. Descriptive analysis indicated that the dataset was predominantly right-skewed (Skp > 0). The GOF results show that the Weibull distribution provides the best representation of the wind speed data (p=0.02), followed by the distributions of Log-normal and Gamma. The Weibull parameter (α > 1) further confirmed its suitability for the data. The findings suggest that Ikeja may experience higher wind speeds in the future, emphasizing the need for precautionary measures to mitigate potential damage to infrastructure and property. This study provides valuable insights for meteorologists and urban planners in anticipating and managing climate-related risks in the city.
Evaluating Nigeria Exchange Group All Share Index: Insights from Linear and GARCH Modeling Techniques
This study investigates the volatility of the Nigeria Exchange Group All Share Index (ASI) using linear regression and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling techniques. Despite prevalent public concerns regarding stock market instability, our analysis reveals that these perceptions are often exaggerated, driven largely by historical price levels and media representation. We employed a linear regression model to analyze monthly historical ASI data from 1985 to 2023, establishing a significant positive relationship between time and ASI values, with an R2 value of 0.7493, indicating that approximately 75% of the variance in ASI can be explained by the model. The Breusch-Godfrey test highlighted significant serial correlation in the residuals, necessitating further analysis using GARCH models to account for time-varying volatility. Our findings suggest that traditional asset pricing models may overlook alter- native risk measures that investors prioritize, emphasizing the need for a more nuanced understanding of market behavior. The adequacy of the model is achieved with a p-value 0.000017. Overall, this study contributes to the existing literature by offering insights into the dynamics of the Nigerian stock market and its volatility patterns, which are crucial for investors and policymakers alike.
Impact of Hypertension and Other Comorbidities on Stroke Subtypes among Patients in Anambra State Teaching Hospital Awka, Nigeria
Stroke continues to be a major cause of illness and death. Its occurrence is greatly affected by modifiable risk factors. This study aimed to describe the demographic and clinical characteristics of stroke patients and to explore the relationship between hypertension and stroke subtype. A retrospective cross-sectional analysis of 264 stroke patients was conducted. Continuous variables using means, medians, and interquartile ranges were summarized. Categorical variables were described with frequencies and percentages. The prevalence estimates with 95% confidence intervals (CIs) were calculated. Fisher’s Exact Test assessed the simple association between hypertension and stroke subtype (ischemic, haemorrhagic, transient ischemic attack [TIA]). A multinomial logistic regression model, adjusted for age and gender were applied, to estimate adjusted odds ratios (aORs) with 95% CIs, using ischemic stroke as the reference category. The average age was 59.1 years (SD = 16.3), and mean BMI was 29.9 (SD = 6.36), indicating a predominance of overweight and obesity (BMI ≥ 25). Hypertension was the most common comorbidity (91.3%, 95% CI: 87.2–94.4), followed by hyperlipidaemia (97%) and high cholesterol (35%). Type 2 diabetes (11%) and heart disease (26.9%) were less common. Fisher’s Exact Test showed no statistically significant association between hypertension status and stroke subtype (p = 0.362). Stroke subtype distribution was as follows: ischemic stroke (58.7%), haemorrhagic stroke (39.0%), and transient ischemic attack (TIA) (2.3%). In logistic regression analysis, hypertension was not significantly related to haemorrhagic stroke (aOR = 1.47, 95% CI: 0.58–3.76) or TIA (aOR = 0.55, 95% CI: 0.06–5.22) compared to ischemic stroke. Stroke patients in this group were mostly older, obese, and had high blood pressure. Although hypertension was quite common, it did not significantly vary by stroke subtype, highlighting its role as a universal risk factor across different types of strokes.
Repetitive Sampling Plans for Life Tests Based on Percentiles of the Half-Normal Distribution with Applications to Software Reliability and Device Lifetime Data
Acceptance Sampling Plans (ASPs) are indispensable statistical tools in quality control for making decisions regarding the acceptance or rejection of product lots. While traditional plans often rely on the mean lifetime, percentile-based criteria offer a more robust measure, particularly for capturing tail behavior in lifetime distributions. This paper introduces a novel repetitive sampling plan (RSP) for life tests based on percentiles of the Half-Normal Distribution (HND). The plan is designed to verify that a specified quantile of the product lifetime exceeds a predefined standard. The design parameters, namely the sample size, acceptance number, and rejection number, are obtained through an optimization model that minimizes the Average Sample Number (ASN) while ensuring that both the producer’s risk and the consumer’s risk constraints are satisfied. Comprehensive tables are presented for various practical scenarios, examining the effects of the percentile ratio, termination time multiplier, and life percentile on the performance of plan. A comparative analysis demonstrates that the proposed RSP consistently requires a smaller ASN than the comparable single sampling plan, confirming its superior efficiency in reducing inspection effort and cost. The practical utility of the methodology is illustrated through a real-life example using software reliability data, showcasing its straightforward implementation and significant advantages for quality assurance in industrial settings.
The Almon Liu-Type M-Estimator for the Distributed Lag Models in the Presence of Multicollinearity and Outliers
The Almon method is widely used for the estimation of the distributed lag models (DLM). The advantage of using the Almon technique lies in its capability to avoid some serious problems that may arise from the direct application of ordinary least squares (OLS). In the Almon technique, the OLS procedure is applied on transformed regressors, and these regressors correlate themselves leading to the problem of multicollinearity. Moreover, in the presence of outliers in the y-direction, the Almon estimator (AE) may become sensitive. The presence of multicollinearity and outliers jointly in the dataset can strongly distort the AE, leading to the unreliable estimation of the lagged coefficients. We propose the Almon Liu-type M-estimator (ALTME) to address the joint issue of multicollinearity and outliers in y-direction. To show that the proposed estimator has an advantage over the AE, the Almon M-estimator (AME), and the Almon ridge M-estimator (ARME), the Monte Carlo Simulation and two real-life numerical examples are given.
Transformation to Achieve Perfect Correlation
Correlation and linear regression are common means to evaluate association and empirical relationships between two or more variables. Such relationships often show significant departure of |r_XY | from unity. Existing transformations to increase correlation fail to achieve perfect correlation. For a bivariate data, the paper proposes transforming Y to y=G.‖x‖‖y‖, which gives r_(X y)=1 where G is the G-inverse of the matrix A=x.x^Tand x, y denote vectors of deviation scores. The concept is extended to perfect linearity between a dependent variable (Y) and a set of independent variables (Multiple linear regressions) or between set of dependent variables and set of independent variables (Canonical regression), avoiding problems of insignificant beta coefficients in univariate and multivariate regression models and outliers. Empirical illustration of G-inverse and extensions for multiple linear regressions and Canonical regressions are also given. The proposed transformation is a novel method of introducing perfect correlation between two variables. Extension of the concept in multiple linear regressions and canonical regression will go a long way in empirical researches in various branches of science. Future studies may include finding distribution of the proposed perfect correlations and comparison of efficacy of our suggested approach against other traditional ones by providing quantitative evidences.
A comprehensive review on the advancement of the Xgamma Distribution
The present article provides a comprehensive review of the development and applications of the Xgamma distribution (XGD). The Xgamma distribution, conceptually parallel to the Lindley distribution, emerges as a combination of the exponential and gamma distributions with an appropriate weighting coefficient. Owing to its flexibility and analytical tractability, the XGD has gained considerable attention in reliability and lifetime data modeling. In recent years, numerous researchers have proposed various extensions and generalizations of the Xgamma distribution by employing diverse transformation techniques and distributional families. These developments have further enhanced its modeling capability and adaptability to complex data structures. A particularly interesting aspect of this review is the discussion of practical applications, where several real data sets used in previous studies are examined to demonstrate the empirical relevance and versatility of the extended forms of the XGD across different scientific and engineering fields.
Copyright (c) 2026 SCOPUA Journal of Applied Statistical Research https://creativecommons.org/licenses/by/4.0
An Enhanced Burr Type III Distribution: Simulation Studies and Practical Applications
In this paper, we introduce a new three-parameter distribution, the New Exponentiated Burr Type III distribution (NEBIII), which is a member of the Generalized (G)-family of continuous distributions. The mathematical features that are derived include the mode, actuarial measures, order statistics, the analytical forms of the density and hazard functions, and explicit formulations for the moment generating function (MGF). The Maximum Likelihood Estimation (MLE) approach is used to estimate the model parameters. A simulated research with different sample sizes is used to evaluate the estimation's efficacy. The versatility and adaptability of the proposed distribution family are demonstrated on four real-world data sets. Additionally, we examine a Mixture of the Exponential and Exponentiated Burr Type III Distributions, identifying various mathematical characteristics and demonstrating their application to actual data.
Power Generalized KM-Transformation for Non-Monotone Failure Rate Distribution
A more useful transformation model, KM Transformation, for reliability and lifetime data analysis is introduced by Kavya & Manoharan (2021). Power generalization technique is the best approach for analysing a parallel system. In this article, we present a new transformation called Power Generalized KM-Transformation (PGKM) to obtain a more appropriate model while monotone and non-monotone behaviour for the failure rate function occurs. We derived the moments, moment generating function, characteristic function, quantiles, etc. for the PGKM transformation of Exponential distribution (PGKME). Distributions of minimum and maximum are obtained. Estimation of parameters of the PGKME distribution is performed via maximum likelihood method, method of moment, and least square estimation method. A simulation study is performed to validate the maximum likelihood estimator (MLE). Analysis of three sets of real data is provided.
Development of a Repetitive Control Chart for Monitoring Processes with Dagum-Distributed Data
This paper presents a repetitive control chart designed for monitoring processes where the quality characteristic follows a Dagum distribution a flexible, skewed distribution often used in income, finance, and reliability data. Traditional control charts assume normality, resulting in poor performance with heavy-tailed or skewed distributions. To address this, we introduce a repetitive control chart based on quantiles of the Dagum distribution. The approach includes deriving control limits, implementing an optimised repetitive sampling scheme, and evaluating performance using ARL. Simulation studies demonstrate that the proposed chart outperforms existing charts in detecting small to moderate shifts in data distributed according to a Dagum distribution.
A new Bartlett-based homogeneity test for linear regression models
The assumption of homogeneous residuals is crucial in linear regression analysis. In fact, all statistical inferences around regression coefficients are built on the basis of this fundamental assumption. As a result, having a linear model with heterogeneous residuals would destruct all those beneficial reliable inferences, and this specifically means that all extracted predicted values, confidence regions and any other conclusions are then false, misleading and far from reality. Also, heterogeneity underestimates true significance levels, which could lead to considering the importance of an explanatory variable whereas truth is not. These are a few consequences of handling a linear regression model with heterogeneous residuals, and hence, researchers have focused on two things: first, defining a consistent statistical tool to catch deviations from homogeneity, and second, proposing approaches to deal with that violation efficiently. We are not interested here in the last goal, and rather, we are focusing on the first one. Indeed, there were enormous and remarkable efforts seeking to fulfil the first goal in different ways, from plotting residuals against fitted values to applying statistical tests, such as the Breusch-Pagan test, Koenker test, and other related tests. But in effect, analysing plots is highly dependent ‎on ‎self-experience and how one would draw conclusions and thoughts about the plot and, in fact, it is not an easy task in many real-world studies. On the other hand, each of the Breusch-Pagan and Koenker tests has its own deficiencies and misleading conclusions. Other related tests, which we will summarise shortly, are not easily implemented or programmed. So, we aim in the present paper‎ to present a simple statistical method for testing the homogeneity assumption of linear regression residuals, by just employing the well-known Bartlett's test‎, on the basis of defining two suitable disjoint subsets driven from the original dataset. We evaluate the proposed approach by a series of simulation studies and analysing a previous historic case study, and it will be shown that the proposed method‎ controls the error rate in a nominal level and has high performance in sense of both homogeneity and heterogeneity detection‎s.
The Exact Analysis of Augmented Incomplete Latin Square Design with One Missing Observation
As Federer's augmented Latin square design (ALSD) is one of the most important augmented designs used in plant breeding programs, this paper aims to introduce an exact analysis for an ALSD with a missing check treatment. Namely, the novelty of this present work is evident in proposing and defining the augmented incomplete Latin square design (AILSD). In this light, the effects of rows, columns, checks, and new treatments were evaluated. All previous known studies were limited by focusing on dealing with complete datasets, paying less attention to the problem of the presence of a missing value, and this is the gap we are trying to fill here. Moreover, computing the regression sum of squares (RSS) for both full and reduced models was essential to figure out the other needed sum of squares. A numerical example and R simulation study were carried out to assess the performance of the proposed design. The importance of employing R comes from filling the calculation gaps involved in analysing AILSD.
On the Mathematical Expectation of the Sample Variance in Simple Sampling Technique
Drawing random samples is the core of modern life jobs. In manufacturing, it is important to inspect ‎deficiencies ‎by ‎only ‎sampling ‎items‎ from a production line, to meet quality worldwide standards and to maintain sufficient statistical quality control‎. Furthermore, in today's survey research, the theory of sampling technique is foundational to ensure that all‎ inquired ‎and ‎essential‎ information is gathered. In effect, one may name thousands of practical applications that rely on taking samples, like climatic studies, industry, ecology, and so on. In effect, many studies were designed and proposed in searching for an effective sampling technique. It is, in fact, both an art and a robust science. So many strategies and considerations were plotted to determine the proper sample size ‎and the proper sampling technique, like simple sampling and stratified sampling. This paper is a brief study focusing on the behaviour of the mathematical expectation of the sample variance in sampling without replacement and in sampling with replacement‎. ‎Formally‎, ‎we show that when sampling is with replacement‎, ‎there exists a crucial difference between the two situations‎, ‎namely‎, ‎distinct samples and indistinct samples‎. Namely, by a series of simulation studies and a famous historical example, it will be shown that there is a faulty fact concerning the unbiasedness of sample variance when drawing indistinct samples with replacement.
In this paper, the power of each test of unimodality/multimodality is estimated. The power of each test is estimated on the basis of the alternative hypothesis that there is bimodality. The Power Curve and Power Envelope of each test of unimodality /multimodality are also shown by using a graphical representation. The most stringent test of all the four tests of unimodality/multimodality is also recognised and finally finds the Conclusion about the most powerful, best test, worst test and most stringent test among these four stated tests.
Joint Parameter Estimation in Measurement Errors and Non-Response for Sensitive Variables
In this paper, joint parameter estimation for mean and variance was considered in the presence of measurement errors and non-response for the Sensitive variable using auxiliary information. The properties of the suggested estimator(s) have been studied. Expressions for bias and mean square error up to first order of approximation are derived, and the theoretical characteristics of the suggested estimators are scrutinised. A numerical study is carried out to observe the performance of the proposed estimators. The scope of this work is to create better estimators that can effectively manage both kinds of mistakes at the same time, especially in randomised response settings where biasing sensitive data is common. Applications to actual data and simulation studies are used to evaluate the estimators' effectiveness. When both measurement error and non-response are present, the results show that the suggested estimators outperform the traditional estimators.
Sum of Weighted Gamma Distribution: Properties and Applications in Reliability Engineering
Applications of the lifetime continuous distributions in the reliability field are always in demand to improve the performance of electronic components. In this paper, we proposed a new method which is used to obtain a single-parameter lifetime distribution named “Sum of Weighted Gamma Distribution” (SWG distribution). The newly proposed distribution is obtained by weighing the gamma distribution with varying shape and constant scale parameters. The idea has been taken from the formation of the Lindley distribution, which is a mixture of exponential (with scale parameter ) and gamma (with shape parameter 2 and scale parameter ) distributions. Various mathematical properties of the SWG distribution have been derived. A few reliability and inequality measures, such as survival function, hazard rate, reversed hazard rate, cumulative hazard rate, Ginni indices, Lorenz and Bonferroni inequalities have been developed. Order statistics and upper record values from the SW-Gamma distribution have been studied. The parameter is estimated by using the method of maximum likelihood estimation (MLE), moreover, a simulation is conducted. Finally, the applications of the SWG distribution have been shown on three different lifetime data sets and compared with famous single-parameter lifetime distributions. It is shown that the SWG distribution is more flexible comparatively.
Statistical analysis to assess the satisfaction level of foreign students in China concerning accommodation provided by the university
The focus of this study was the assessment of the satisfaction level regarding accommodation provided by the University to International students studying in China. Southwest University of Political Science & Law, China was taken as a case study to complete this study. A questionnaire was designed and 152 international students studying at the Yubei campus were approached personally. The students filled out the questionnaires based on the main five variables i.e. overview, bedrooms, dormitory services, study environment, and cultural exchange. SPSS and SmartPLS were used for data analysis. The factor loadings for all items exceed the threshold of 0.6, indicating strong associations between the items and their corresponding constructs. The Variance Inflation Factor (VIF) values, which measure multicollinearity, are all below 3, confirming that there are no significant redundancy issues among the items. Cronbach’s alpha values for all constructs are above 0.7, demonstrating good internal consistency reliability. The Average Variance Extracted (AVE) values, which assess convergent validity, are all above 0.5, indicating that each construct explains more than half of the variance in its items. About a majority of SWUPL international students show considerably high satisfaction with university housing facilities, along with the student assistance structure and out-of-classroom possibilities. The positive experiences reported by students directly related to the strong cooperation of SWUPL’s International Student Office and the university’s commitment to academic and non-academic activities.
A New Generalization of Exponentiated Exponential Distribution using Quantile Functions
Generalising existing probability distributions increases their appeal to researchers and expands their applicability to real-life situations by adding flexibility to the existing models. In this study, a new generalised class of the exponentiated exponential (EE) distribution is introduced, referred to as the T-Exponential Exponential{Y} or T-EE{Y} class of distributions. By utilising the quantile functions of several well-known continuous distributions in the T-EE{Y} framework, six distinct subclasses have been developed. Various statistical properties such as quantiles, mode, incomplete moments, entropy, and mean deviation have been derived for these subclasses. Additionally, four specific member models within the proposed class have been explored. The study demonstrates that the members from the T-EE{Y} class exhibit flexible shapes, including unimodal, bimodal, symmetrical, and skewed (both right and left) forms. Parameter estimation is performed using the maximum likelihood estimation method, and the effectiveness of the estimators is evaluated through a Monte Carlo simulation study. To evaluate the practical applicability of the proposed class of distributions, three real-world datasets are analysed. The members of the proposed class consistently outperform several existing distributions in modelling lifetime data, showcasing its significance and versatility.
Shako Kumaraswamy distributions with properties and applications in different fields
The Shako Kumaraswamy distribution is a novel and adaptable extension of the Kumaraswamy distribution that is presented in this paper. By adding a shape parameter, the suggested model expands on the traditional Kumaraswamy distribution and improves its ability to present a variety of distributional shapes and tail behaviours. The probability density function, quantile function, cumulative distribution function, moments, and other reliability metrics are all derived as part of a thorough mathematical analysis. Additionally, limiting behaviours and special situations are analysed to demonstrate the generality of the suggested distribution. The behaviour of the estimators under various sample sizes and parameter settings is evaluated in a thorough Monte Carlo simulation study to evaluate the effectiveness of the parameter estimation techniques. Additionally, the Shako Kumaraswamy distribution's practicality is illustrated using two real-world data sets from different domains.
A new generalized Logistic class of distributions: Properties and applications on flood and earthquake data sets with bivariate extension
For univariate and bi-variate data, we propose a new generalized logistic class of distributions exible enough to exhibit monotone and non-monotone hazard rates shapes. The physical interpretation of the new family preludes in the context of series-parallel structures. Its mathematical features, including a valuable expansion for the density, explicit formulations for the quantile function, ordinary and incomplete moments, and generating function, are all derived. The parameter estimation of the new family is done using the maximum likelihood method. One of the unique model, called the generalized logistic Burr-III, is thoroughly investigated in applied sense. The exible density and hazard rate shapes capacitates the model to be applicable in extreme value theory. For univariate case, two real-life data sets related to hydrology and seismic activity have been employed to solidify the superiority of the proposed distribution to ve well established families. For bivariate data, initially a bivariate extension of the proposed family is established analytically with the help of empirical ndings. Then, a real bi-variate data related to operational lifespan of two components of a computer, has been studied using bivariate generalized logistic Burr-III model and the results are reported.
A comprehensive study of the extended JCA distribution with properties and applications
The JCA distribution, which was first proposed by Jamal et al. (2021), was expanded in this paper. The new model is called the extended JCA distribution, or EJCA. For the researchers, this will be more adaptable and appealing. Explicit expressions for the moment generating function, the analytical form of the density and hazard, order statistics, and mode are among the mathematical properties that are derived. The maximum likelihood approach is used to estimate the model parameters. To evaluate the effectiveness of the estimation, a simulation study with a range of sample sizes is conducted. Two applications to the real-world data set demonstrate the proposed family's adaptability. We combined the classical exponential distribution with the JCA distribution. The new model is extremely flexible and combines the features of both models, including exponential with some probabilities and JCA. For the new distribution, we derive several mathematical properties and applications to actual data sets.