Iranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201Bayesian optimal designs for nonlinear models with three or four parameters11573368710.22034/jirss.2025.2071511.1135ENManizhehGoudarziDepartment of Mathematics, Faculty of Basic Sciences, Ayatollah Boroujerdi University, Boroujerd, Iran.MehdiJafariDepartment of Mechanical Engineering, Sahand University of Technology, Tabriz, Iran.Journal Article20250912In design of experiments, optimal design is an important approach that maximizes the chances of experimental success. A- and D-optimality are well-known criteria for identifying optimal designs. In nonlinear models, these criteria depend on unknown parameters, complicating the design derivation. This paper uses the Bayesian method to address this, deriving A- and D-optimal designs for EMAX, log-linear, and LINEXP models with three or four parameters, using uniform priors. Optimal designs with minimum support points are obtained, with varying weights. These designs serve as benchmarks for evaluating practical alternative designs. Two alternatives were assessed, showing over 80\% efficiency in most models compared to A- and D-optimal designs. The computations in this study were performed using a numerical nonlinear approach, specifically the NLPSolve method, which is included in the Optimization package in Maple software.https://jirss.irstat.ir/article_733687_43218ae87b13d217aa4f260ebcadd775.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201Statistical Learning in the Fight against COVID-19: A Focus on Diagnosis163273368810.22034/jirss.2025.2053767.1102ENMostafaTamandiDepartment of Statistics, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.ZahraKamiabDepartment of Community Medicine, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran0000-0001-6670-1828FatemehBahrehmandClinical Research Development Unit, Ali-Ibn Abi-Talib Hospital, Rafsanjan University of Medical Sciences, Rafsanjan, Iran;
Department of Internal Medicine, Ali-Ibn Abi-Talib Hospital, School of Medicine, Rafsanjan University of Medical0000-0002-3966-597XMohammad YasinZamanianDepartment of Physiology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran;
Department of Pharmacology and Toxicology, School of Pharmacy, Hamadan University of Medical Sciences, Hamadan, Iran.0000-0003-0944-0320MohammadrezaGholamrezapourClinical Research Development Unit, Ali-Ibn Abi-Talib Hospital, Rafsanjan University of Medical Sciences, Rafsanjan, Iran; Department of Internal Medicine, Ali-Ibn Abi-Talib Hospital, School of Medicine, Rafsanjan University of Medical0000-0002-5571-0611Seyed Mohammad EbrahimPourhosseiniClinical Research Development Unit, Ali-Ibn Abi-Talib Hospital, Rafsanjan University of Medical Sciences, Rafsanjan, Iran;
Department of Internal Medicine, Ali-Ibn Abi-Talib Hospital, School of Medicine, Rafsanjan University of Medical0000-0002-4218-6734GholamrezaBazmandeganPhysiology-Pharmacology Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
Department of Physiology and Pharmacology, School of Medicine, Rafsanjan University of0000-0002-5379-5623Journal Article20250218The accurate diagnosis of infectious diseases such as COVID-19 requires statistically reliable classification methods capable of handling complex, heterogeneous, and imbalanced data. In this study, several statistical and machine learning algorithms --logistic regression, linear discriminant analysis, k-nearest neighbors, decision tree, and random forest --were comparatively evaluated using clinical and laboratory data from 506 hospitalized patients in Rafsanjan, Iran. The dataset included 27 categorical and 11 quantitative variables. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was employed. Model performance was assessed using a comprehensive set of criteria, including accuracy, sensitivity, specificity, positive and negative predictive values (NPV), and the area under the ROC curve. The comparative analysis showed that RF and LR achieved the best overall performance, while SMOTE improved sensitivity and NPV at the expense of specificity. The findings emphasize the importance of appropriate imbalance correction and multi-metric evaluation in developing statistically robust diagnostic models for medical data.https://jirss.irstat.ir/article_733688_19b02d9fd31b1a6690b7acc24b3b6d2d.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201A Quantile Based Generalized Cross Entropy of Order Statistics334973368910.22034/jirss.2025.2030759.1064ENRajeshGanapathiCochin University of Science and TechnologyTincyPhilipCochin University of Science and TechnologyJournal Article20240531In this paper, we propose a generalized cross entropy between the distributions of the ith order statistic and the parent random variable X, defined using the quantile function. This method is more flexible than traditional PDF-based measures, particularly in situations where estimating the underlying density is difficult or unreliable. We investigate the properties of this measure and present examples to illustrate these concepts. Furthermore, we introduce a residual version of the quantile based generalized cross entropy between the distributions of the ith order statistic and the parent random variable X, along with some characterization results. Comparative analyses using simulation and real data indicate that the proposed measure provides improved interpretability and robustness relative to the quantile based Kerridge inaccuracy measure. This study effectively connects theoretical development with practical application, contributing to the field of statistical analysis.https://jirss.irstat.ir/article_733689_a6fa0dc13c61dc928712789c96ee90a5.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201Likelihood Ratio Ordering for Spacings Arising from Multiple-Outlier Models506873369110.22034/jirss.2026.2076023.1150ENEbrahimAmini-SereshtDepartment of Statistics, Faculty of Science, Bu-Ali Sina University, Hamedan, Iran.0000-0002-4785-7293SalmanIzadkhahDepartment of Statistics, Campus of Bijar, University of Kurdistan, Bijar, Iran.Journal Article20251029In this paper, we investigate the stochastic properties of spacings among order statistics derived from a sample of independent, non-negative random variables that are divided into two groups with different distributions. Previous studies have shown that when these distribution functions are exponential distributions with specified hazard rates, the likelihood ratio ordering holds among the spacings under specific conditions. The present work extends these results by considering more general continuous distribution functions. We identify the necessary conditions on the parent distribution functions for preserving the likelihood ratio ordering among spacings in general settings. The comparison results enhance our understanding of stochastic ordering theory and provide valuable insights for applications in reliability, survival analysis, and related fields, aiding in the development of more flexible and accurate statistical models.https://jirss.irstat.ir/article_733691_0adac43b46458420aed8e39aae463045.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201Influence diagnostics for the Gamma-Pareto regression: a DFFITS-based comparison of residuals699073369310.22034/jirss.2026.2076260.1154ENNasirSaleemDepartment of Statistics, Bahauddin Zakariya university, Multan Pakistan.0000-0001-6556-4737AtifAkbarDepartment of Statistics, Bahauddin Zakariya University (BZU), Multan, Pakistan,608000000-0003-3227-9731AsadAbbasDepartment of Statistics
Bahauddin Zakariya University (BZU), Multan, Pakistan,60800Journal Article20251030The generalized linear models (GLMs) use Gamma-Pareto regression Model (G-PRM) to address the sensitivity of influential observations. Difference of Fits (DFFITS) is a popular technique for identifying influential observations. We apply DFFITS to the G-PRM with various residuals. We present illustrative real and simulated data. One class of adjusted Pearson residuals is more effective in detecting influential observations, considering small or large dispersion parameters. We calculate detection percentages to evaluate the proposed procedure's performance, replicating the process 10,000 times.<br><br>The generalized linear models (GLMs) use Gamma-Pareto regression Model (G-PRM) to address the sensitivity of influential observations. Difference of Fits (DFFITS) is a popular technique for identifying influential observations. We apply DFFITS to the G-PRM with various residuals. We present illustrative real and simulated data. One class of adjusted Pearson residuals is more effective in detecting influential observations, considering small or large dispersion parameters. We calculate detection percentages to evaluate the proposed procedure's performance, replicating the process 10,000 times.https://jirss.irstat.ir/article_733693_e1035396534d80dc520be81afe6b3cd0.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201Markov SIRD Epidemic Model with Semi-Markov Sojourn-Time Analysis of COVID-199110673369210.22034/jirss.2026.2073280.1143ENFaihatuzZuhairohDepartment of Mathematics Education, STKIP YPUP Makassar, South Sulawesi, Indonesia0000-0001-7524-3160M. RaisRidwanStudy Program of Mathematics Education, STKIP YPUP Makassar, South Sulawesi, Indonesia0000-0003-1747-5128Journal Article20251001The SIRD (Susceptible–Infected–Recovered–Deceased) model is a standard framework for analyzing infectious disease dynamics. Classical continuous-time Markov formulations assume constant transition rates and memoryless (exponential) sojourn-times, which may oversimplify empirical epidemic processes. In this study, the Markov SIRD model is employed as a baseline, with transition parameters estimated analytically via maximum likelihood. To assess the validity of the exponential sojourn-time assumption, a semi-Markov framework is introduced exclusively for duration analysis of the infected state. Specifically, the sojourn-times associated with recovery (I --> R) and death (I -->D) transitions are modeled using exponential and Weibull distributions and compared using likelihood-based criteria. Using COVID-19 data from the Special Region of Yogyakarta, Indonesia, the results show that Weibull distributions provide a substantially better fit than the exponential assumption for both recovery and mortality durations. These findings indicate significant deviations from the memoryless assumption underlying the Markov model. This study does not construct a full dynamic semi-Markov epidemic simulator; instead, the semi-Markov framework is used to statistically characterize and evaluate the temporal structure of infected-state durations. The results highlight the importance of realistic sojourn-time modeling for understanding epidemic progression and for assessing the limitations of classical Markov-based epidemic models.https://jirss.irstat.ir/article_733692_8122a0dfcfe480a820d42af9c7afd5d8.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201Support vector fuzzy regression with fuzzy input-fuzzy output and fuzzy error10712473513110.22034/jirss.2026.2075601.1148ENAzamMoghadamDepartment of Statistics, Faculty of Mathematical Sciences and Statistics, University of Birjand, Birjand, Iran.MohsenArefiDepartment of Statistics, Univrsity of Birjand, Birjand IranJournal Article20251023In this paper, we investigate a new approach of fuzzy regression analysis based on support vectors when the available data and error variable are fuzzy quantities. In this approach, based on the concept of the distance between two parallel hyper planes, we obtain the marginal hyper planes and then, based on some constraints on the fuzzy data, we present an optimization problem to estimate the parameters of fuzzy regression model. The proposed method is investigated in two cases: with fuzzy fixed error and with fuzzy variable errors. To evaluate the proposed support vector fuzzy regression (SVFR) models, we present two indices of goodness of fit. Based on these indices, the presented SVFR models are compared with some other approaches on the numerical and simulated examples.https://jirss.irstat.ir/article_735131_83d1078de280d0efaea83bf7120a2172.pdfIranian Statistical SocietyJournal of the Iranian Statistical Society1726-405724220251201Investigating and Evaluating Credit Risk in Banks Using Support Vector Machines with Genetic Algorithms12513873369010.22034/jirss.2025.2009127.1031ENRaziehAkhondzardainiPhD candidate healthcare Management, Student Research committee, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran0000-0001-8411-6956HamidehShekariKhalilKalavanimodirtPhD candidate healthcare Management, Student Research committee, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iranbzmed@gmail.comJournal Article20230813The prediction of credit risk is of great economic importance for banks and financial institutions, leading to the utilization of various methods in developing predictive models. This study introduces a credit risk prediction model that combines the support vector machine (SVM) with a genetic algorithm (GA) to aid credit decision-making by managers. While SVM is a reliable classification method, its performance can be influenced by factors such as model shape, parameter setting, and feature selection. To address these challenges, a novel approach is proposed that employs GA to optimize feature selection and parameter settings within the SVM framework.The proposed model is compared against alternative models including neural network, logistic regression, random forest, and decision tree. The study utilizes data from Bank of Yazd Province, with a sample size of 1876 customers divided into two groups: those who defaulted on their credit obligations and those who fulfilled them. The results demonstrate that the GA-SVM model serves as a suitable alternative for credit risk prediction, outperforming other models in terms of predictive power. Furthermore, the proposed model offers the benefit of feature selection, enabling financial institutions to identify potential risks and implement preventive measures. The use of GA in conjunction with SVM also facilitates the identification of optimal SVM parameter values, thereby enhancing the overall performance of the model. In conclusion, the proposed GA-SVM model emerges as a valuable tool for credit decision-making and risk management within banks and financial institutions. Further optimization can be achieved by exploring other meta-heuristic optimization algorithms.https://jirss.irstat.ir/article_733690_e34f8625f14904ee3a59fcc2d9db7868.pdf